query: The query consists of users’ needs, leading to several research questions that span a paragraph. Each candidate passage is an abstract from a scientific paper. The objective of this information retrieval task is to identify the abstract that most effectively meets the user’s needs in the query : I am seeking alternatives to Generative Adversarial Networks (GANs) that can be applied to image datasets, such as CIFAR-10. The alternative should be capable of generating new data points based on the original data distribution and should perform comparably to GANs across various metrics. Could you provide information on the standard metrics typically used to evaluate the performance of GANs? I anticipate that this alternative method would initially estimate and model the original data distribution, possibly using a neural network, and then generate diverse data points that adhere to the same distribution through an intelligent sampling technique. However, I am open to learning about other promising approaches as well.

positive:
This paper presents a methodology and workflow that overcome the limitations
of the conventional Generative Adversarial Networks (GANs) for geological
facies modeling. It attempts to improve the training stability and guarantee
the diversity of the generated geology through interpretable latent vectors.
The resulting samples are ensured to have the equal probability (or an unbiased
distribution) as from the training dataset. This is critical when applying GANs
to generate unbiased and representative geological models that can be further
used to facilitate objective uncertainty evaluation and optimal decision-making
in oil field exploration and development.
  We proposed and implemented a new variant of GANs called Info-WGAN for the
geological facies modeling that combines Information Maximizing Generative
Adversarial Network (InfoGAN) with Wasserstein distance and Gradient Penalty
(GP) for learning interpretable latent codes as well as generating stable and
unbiased distribution from the training data. Different from the original GAN
design, InfoGAN can use the training images with full, partial, or no labels to
perform disentanglement of the complex sedimentary types exhibited in the
training dataset to achieve the variety and diversity of the generated samples.
This is accomplished by adding additional categorical variables that provide
disentangled semantic representations besides the mere randomized latent vector
used in the original GANs. By such means, a regularization term is used to
maximize the mutual information between such latent categorical codes and the
generated geological facies in the loss function.
  Furthermore, the resulting unbiased sampling by Info-WGAN makes the data
conditioning much easier than the conventional GANs in geological modeling
because of the variety and diversity as well as the equal probability of the
unconditional sampling by the generator.

positive:
We introduce a new generative model where samples are produced via Langevin
dynamics using gradients of the data distribution estimated with score
matching. Because gradients can be ill-defined and hard to estimate when the
data resides on low-dimensional manifolds, we perturb the data with different
levels of Gaussian noise, and jointly estimate the corresponding scores, i.e.,
the vector fields of gradients of the perturbed data distribution for all noise
levels. For sampling, we propose an annealed Langevin dynamics where we use
gradients corresponding to gradually decreasing noise levels as the sampling
process gets closer to the data manifold. Our framework allows flexible model
architectures, requires no sampling during training or the use of adversarial
methods, and provides a learning objective that can be used for principled
model comparisons. Our models produce samples comparable to GANs on MNIST,
CelebA and CIFAR-10 datasets, achieving a new state-of-the-art inception score
of 8.87 on CIFAR-10. Additionally, we demonstrate that our models learn
effective representations via image inpainting experiments.

positive:
Generative models are undoubtedly a hot topic in Artificial Intelligence,
among which the most common type is Generative Adversarial Networks (GANs).
These architectures let one synthesise artificial datasets by implicitly
modelling the underlying probability distribution of a real-world training
dataset. With the introduction of Conditional GANs and their variants, these
methods were extended to generating samples conditioned on ancillary
information available for each sample within the dataset. From a practical
standpoint, however, one might desire to generate data conditioned on partial
information. That is, only a subset of the ancillary conditioning variables
might be of interest when synthesising data. In this work, we argue that
standard Conditional GANs are not suitable for such a task and propose a new
Adversarial Network architecture and training strategy to deal with the ensuing
problems. Experiments illustrating the value of the proposed approach in digit
and face image synthesis under partial conditioning information are presented,
showing that the proposed method can effectively outperform the standard
approach under these circumstances.

positive:
We consider inpainting in an unsupervised setting where there is neither
access to paired nor unpaired training data. The only available information is
provided by the uncomplete observations and the inpainting process statistics.
In this context, an observation should give rise to several plausible
reconstructions which amounts at learning a distribution over the space of
reconstructed images. We model the reconstruction process by using a
conditional GAN with constraints on the stochastic component that introduce an
explicit dependency between this component and the generated output. This
allows us sampling from the latent component in order to generate a
distribution of images associated to an observation. We demonstrate the
capacity of our model on several image datasets: faces (CelebA), food images
(Recipe-1M) and bedrooms (LSUN Bedrooms) with different types of imputation
masks. The approach yields comparable performance to model variants trained
with additional supervision.

positive:
In recent years, research on image generation methods has been developing
fast. The auto-encoding variational Bayes method (VAEs) was proposed in 2013,
which uses variational inference to learn a latent space from the image
database and then generates images using the decoder. The generative
adversarial networks (GANs) came out as a promising framework, which uses
adversarial training to improve the generative ability of the generator.
However, the generated pictures by GANs are generally blurry. The deep
convolutional generative adversarial networks (DCGANs) were then proposed to
leverage the quality of generated images. Since the input noise vectors are
randomly sampled from a Gaussian distribution, the generator has to map from a
whole normal distribution to the images. This makes DCGANs unable to reflect
the inherent structure of the training data. In this paper, we propose a novel
deep model, called generative adversarial networks with decoder-encoder output
noise (DE-GANs), which takes advantage of both the adversarial training and the
variational Bayesain inference to improve the performance of image generation.
DE-GANs use a pre-trained decoder-encoder architecture to map the random
Gaussian noise vectors to informative ones and pass them to the generator of
the adversarial networks. Since the decoder-encoder architecture is trained by
the same images as the generators, the output vectors could carry the intrinsic
distribution information of the original images. Moreover, the loss function of
DE-GANs is different from GANs and DCGANs. A hidden-space loss function is
added to the adversarial loss function to enhance the robustness of the model.
Extensive empirical results show that DE-GANs can accelerate the convergence of
the adversarial training process and improve the quality of the generated
images.

positive:
Deep generative models (DGMs) of images are now sufficiently mature that they
produce nearly photorealistic samples and obtain scores similar to the data
distribution on heuristics such as Frechet Inception Distance (FID). These
results, especially on large-scale datasets such as ImageNet, suggest that DGMs
are learning the data distribution in a perceptually meaningful space and can
be used in downstream tasks. To test this latter hypothesis, we use
class-conditional generative models from a number of model
classes---variational autoencoders, autoregressive models, and generative
adversarial networks (GANs)---to infer the class labels of real data. We
perform this inference by training an image classifier using only synthetic
data and using the classifier to predict labels on real data. The performance
on this task, which we call Classification Accuracy Score (CAS), reveals some
surprising results not identified by traditional metrics and constitute our
contributions. First, when using a state-of-the-art GAN (BigGAN-deep), Top-1
and Top-5 accuracy decrease by 27.9\% and 41.6\%, respectively, compared to the
original data; and conditional generative models from other model classes, such
as Vector-Quantized Variational Autoencoder-2 (VQ-VAE-2) and Hierarchical
Autoregressive Models (HAMs), substantially outperform GANs on this benchmark.
Second, CAS automatically surfaces particular classes for which generative
models failed to capture the data distribution, and were previously unknown in
the literature. Third, we find traditional GAN metrics such as Inception Score
(IS) and FID neither predictive of CAS nor useful when evaluating non-GAN
models. Furthermore, in order to facilitate better diagnoses of generative
models, we open-source the proposed metric.

positive:
Recent advances in deep learning led to novel generative modeling techniques
that achieve unprecedented quality in generated samples and performance in
learning complex distributions in imaging data. These new models in medical
image computing have important applications that form clinically relevant and
very challenging unsupervised learning problems. In this paper, we explore the
feasibility of using state-of-the-art auto-encoder-based deep generative
models, such as variational and adversarial auto-encoders, for one such task:
abnormality detection in medical imaging. We utilize typical, publicly
available datasets with brain scans from healthy subjects and patients with
stroke lesions and brain tumors. We use the data from healthy subjects to train
different auto-encoder based models to learn the distribution of healthy images
and detect pathologies as outliers. Models that can better learn the data
distribution should be able to detect outliers more accurately. We evaluate the
detection performance of deep generative models and compare them with non-deep
learning based approaches to provide a benchmark of the current state of
research. We conclude that abnormality detection is a challenging task for deep
generative models and large room exists for improvement. In order to facilitate
further research, we aim to provide carefully pre-processed imaging data
available to the research community.

positive:
A learned generative model often produces biased statistics relative to the
underlying data distribution. A standard technique to correct this bias is
importance sampling, where samples from the model are weighted by the
likelihood ratio under model and true distributions. When the likelihood ratio
is unknown, it can be estimated by training a probabilistic classifier to
distinguish samples from the two distributions. We employ this likelihood-free
importance weighting method to correct for the bias in generative models. We
find that this technique consistently improves standard goodness-of-fit metrics
for evaluating the sample quality of state-of-the-art deep generative models,
suggesting reduced bias. Finally, we demonstrate its utility on representative
applications in a) data augmentation for classification using generative
adversarial networks, and b) model-based policy evaluation using off-policy
data.

positive:
Generative adversarial networks (GANs) have attracted intense interest in the
field of generative models. However, few investigations focusing either on the
theoretical analysis or on algorithm design for the approximation ability of
the generator of GANs have been reported. This paper will first theoretically
analyze GANs' approximation property. Similar to the universal approximation
property of the fully connected neural networks with one hidden layer, we prove
that the generator with the input latent variable in GANs can universally
approximate the potential data distribution given the increasing hidden
neurons. Furthermore, we propose an approach named stochastic data generation
(SDG) to enhance GANs'approximation ability. Our approach is based on the
simple idea of imposing randomness through data generation in GANs by a prior
distribution on the conditional probability between the layers. SDG approach
can be easily implemented by using the reparameterization trick. The
experimental results on synthetic dataset verify the improved approximation
ability obtained by this SDG approach. In the practical dataset, four GANs
using SDG can also outperform the corresponding traditional GANs when the model
architectures are smaller.

positive:
Generative models for deep learning are promising both to improve
understanding of the model, and yield training methods requiring fewer labeled
samples.
  Recent works use generative model approaches to produce the deep net's input
given the value of a hidden layer several levels above. However, there is no
accompanying "proof of correctness" for the generative model, showing that the
feedforward deep net is the correct inference method for recovering the hidden
layer given the input. Furthermore, these models are complicated.
  The current paper takes a more theoretical tack. It presents a very simple
generative model for RELU deep nets, with the following characteristics: (i)
The generative model is just the reverse of the feedforward net: if the forward
transformation at a layer is $A$ then the reverse transformation is $A^T$.
(This can be seen as an explanation of the old weight tying idea for denoising
autoencoders.) (ii) Its correctness can be proven under a clean theoretical
assumption: the edge weights in real-life deep nets behave like random numbers.
Under this assumption ---which is experimentally tested on real-life nets like
AlexNet--- it is formally proved that feed forward net is a correct inference
method for recovering the hidden layer.
  The generative model suggests a simple modification for training: use the
generative model to produce synthetic data with labels and include it in the
training set. Experiments are shown to support this theory of random-like deep
nets; and that it helps the training.

positive:
Multimodal generative models should be able to learn a meaningful latent
representation that enables a coherent joint generation of all modalities
(e.g., images and text). Many applications also require the ability to
accurately sample modalities conditioned on observations of a subset of the
modalities. Often not all modalities may be observed for all training data
points, so semi-supervised learning should be possible. In this study, we
propose a novel product-of-experts (PoE) based variational autoencoder that
have these desired properties. We benchmark it against a mixture-of-experts
(MoE) approach and an approach of combining the modalities with an additional
encoder network. An empirical evaluation shows that the PoE based models can
outperform the contrasted models. Our experiments support the intuition that
PoE models are more suited for a conjunctive combination of modalities.

positive:
Insufficient training data is a major bottleneck for most deep learning
practices, not least in medical imaging where data is difficult to collect and
publicly available datasets are scarce due to ethics and privacy. This work
investigates the use of synthetic images, created by generative adversarial
networks (GANs), as the only source of training data. We demonstrate that for
this application, it is of great importance to make use of multiple GANs to
improve the diversity of the generated data, i.e. to sufficiently cover the
data distribution. While a single GAN can generate seemingly diverse image
content, training on this data in most cases lead to severe over-fitting. We
test the impact of ensembled GANs on synthetic 2D data as well as common image
datasets (SVHN and CIFAR-10), and using both DCGANs and progressively growing
GANs. As a specific use case, we focus on synthesizing digital pathology
patches to provide anonymized training data.

positive:
Deep neural network approaches to inverse imaging problems have produced
impressive results in the last few years. In this paper, we consider the use of
generative models in a variational regularisation approach to inverse problems.
The considered regularisers penalise images that are far from the range of a
generative model that has learned to produce images similar to a training
dataset. We name this family \textit{generative regularisers}. The success of
generative regularisers depends on the quality of the generative model and so
we propose a set of desired criteria to assess generative models and guide
future research. In our numerical experiments, we evaluate three common
generative models, autoencoders, variational autoencoders and generative
adversarial networks, against our desired criteria. We also test three
different generative regularisers on the inverse problems of deblurring,
deconvolution, and tomography. We show that restricting solutions of the
inverse problem to lie exactly in the range of a generative model can give good
results but that allowing small deviations from the range of the generator
produces more consistent results.

positive:
Combining Generative Adversarial Networks (GANs) with encoders that learn to
encode data points has shown promising results in learning data representations
in an unsupervised way. We propose a framework that combines an encoder and a
generator to learn disentangled representations which encode meaningful
information about the data distribution without the need for any labels. While
current approaches focus mostly on the generative aspects of GANs, our
framework can be used to perform inference on both real and generated data
points. Experiments on several data sets show that the encoder learns
interpretable, disentangled representations which encode descriptive properties
and can be used to sample images that exhibit specific characteristics.

positive:
As a new approach to train generative models, \emph{generative adversarial
networks} (GANs) have achieved considerable success in image generation. This
framework has also recently been applied to data with graph structures. We
propose labeled-graph generative adversarial networks (LGGAN) to train deep
generative models for graph-structured data with node labels. We test the
approach on various types of graph datasets, such as collections of citation
networks and protein graphs. Experiment results show that our model can
generate diverse labeled graphs that match the structural characteristics of
the training data and outperforms all alternative approaches in quality and
generality. To further evaluate the quality of the generated graphs, we use
them on a downstream task of graph classification, and the results show that
LGGAN can faithfully capture the important aspects of the graph structure.

positive:
Generative models are known to be difficult to assess. Recent works,
especially on generative adversarial networks (GANs), produce good visual
samples of varied categories of images. However, the validation of their
quality is still difficult to define and there is no existing agreement on the
best evaluation process. This paper aims at making a step toward an objective
evaluation process for generative models. It presents a new method to assess a
trained generative model by evaluating the test accuracy of a classifier
trained with generated data. The test set is composed of real images.
Therefore, The classifier accuracy is used as a proxy to evaluate if the
generative model fit the true data distribution. By comparing results with
different generated datasets we are able to classify and compare generative
models. The motivation of this approach is also to evaluate if generative
models can help discriminative neural networks to learn, i.e., measure if
training on generated data is able to make a model successful at testing on
real settings. Our experiments compare different generators from the
Variational Auto-Encoders (VAE) and Generative Adversarial Network (GAN)
frameworks on MNIST and fashion MNIST datasets. Our results show that none of
the generative models is able to replace completely true data to train a
discriminative model. But they also show that the initial GAN and WGAN are the
best choices to generate on MNIST database (Modified National Institute of
Standards and Technology database) and fashion MNIST database.

positive:
Neural random fields (NRFs), referring to a class of generative models that
use neural networks to implement potential functions in random fields (a.k.a.
energy-based models), are not new but receive less attention with slow
progress. Different from various directed graphical models such as generative
adversarial networks (GANs), NRFs provide an interesting family of undirected
graphical models for generative modeling. In this paper we propose a new
approach, the inclusive-NRF approach, to learning NRFs for continuous data
(e.g. images), by introducing inclusive-divergence minimized auxiliary
generators and developing stochastic gradient sampling in an augmented space.
Based on the new approach, specific inclusive-NRF models are developed and
thoroughly evaluated in two important generative modeling applications - image
generation and anomaly detection. The proposed models consistently improve over
state-of-the-art results in both applications. Remarkably, in addition to
superior sample generation, one additional benefit of our inclusive-NRF
approach is that, unlike GANs, it can directly provide (unnormalized) density
estimate for sample evaluation. With these contributions and results, this
paper significantly advances the learning and applications of NRFs to a new
level, both theoretically and empirically, which have never been obtained
before.

positive:
We consider the problem of learning deep generative models from data. We
formulate a method that generates an independent sample via a single
feedforward pass through a multilayer perceptron, as in the recently proposed
generative adversarial networks (Goodfellow et al., 2014). Training a
generative adversarial network, however, requires careful optimization of a
difficult minimax program. Instead, we utilize a technique from statistical
hypothesis testing known as maximum mean discrepancy (MMD), which leads to a
simple objective that can be interpreted as matching all orders of statistics
between a dataset and samples from the model, and can be trained by
backpropagation. We further boost the performance of this approach by combining
our generative network with an auto-encoder network, using MMD to learn to
generate codes that can then be decoded to produce samples. We show that the
combination of these techniques yields excellent generative models compared to
baseline approaches as measured on MNIST and the Toronto Face Database.

negative:
With the increasing interest in the content creation field in multiple
sectors such as media, education, and entertainment, there is an increasing
trend in the papers that uses AI algorithms to generate content such as images,
videos, audio, and text. Generative Adversarial Networks (GANs) in one of the
promising models that synthesizes data samples that are similar to real data
samples. While the variations of GANs models, in general, have been covered to
some extent in several survey papers, to the best of our knowledge, this is
among the first survey papers that reviews the state-of-the-art video GANs
models. This paper first categorized GANs review papers into general GANs
review papers, image GANs review papers, and special field GANs review papers
such as anomaly detection, medical imaging, or cybersecurity. The paper then
summarizes the main improvements in GANs frameworks that are not initially
developed for the video domain but have been adopted in multiple video GANs
variations. Then, a comprehensive review of video GANs models is provided under
two main divisions according to the presence or non-presence of a condition.
The conditional models then further grouped according to the type of condition
into audio, text, video, and image. The paper is concluded by highlighting the
main challenges and limitations of the current video GANs models. A
comprehensive list of datasets, applied loss functions, and evaluation metrics
is provided in the supplementary material.

negative:
Measuring the generalization capacity of Deep Generative Models (DGMs) is
difficult because of the curse of dimensionality. Evaluation metrics for DGMs
such as Inception Score, Fr\'echet Inception Distance, Precision-Recall, and
Neural Net Divergence try to estimate the distance between the generated
distribution and the target distribution using a polynomial number of samples.
These metrics are the target of researchers when designing new models. Despite
the claims, it is still unclear how well can they measure the generalization
capacity of a generative model. In this paper, we investigate the capacity of
these metrics in measuring the generalization capacity. We introduce a
framework for comparing the robustness of evaluation metrics. We show that
better scores in these metrics do not imply better generalization. They can be
fooled easily by a generator that memorizes a small subset of the training set.
We propose a fix to the NND metric to make it more robust to noise in the
generated data. Toward building a robust metric for generalization, we propose
to apply the Minimum Description Length principle to the problem of evaluating
DGMs. We develop an efficient method for estimating the complexity of
Generative Latent Variable Models (GLVMs). Experimental results show that our
metric can effectively detect training set memorization and distinguish GLVMs
of different generalization capacities. Source code is available at
https://github.com/htt210/GeneralizationMetricGAN.

negative:
Deep generative models, such as Generative Adversarial Networks (GANs) or
Variational Autoencoders (VAs) have been demonstrated to produce images of high
visual quality. However, the existing hardware severely limits the size of the
images that can be generated. The rapid growth of high dimensional data in many
fields of science therefore poses a significant challenge for generative
models. In cosmology, the large-scale, three-dimensional matter distribution,
modeled with N-body simulations, plays a crucial role in understanding the
evolution of the universe. As these simulations are computationally very
expensive, GANs have recently generated interest as a possible method to
emulate these datasets, but they have been, so far, mostly limited to two
dimensional data. In this work, we introduce a new benchmark for the generation
of three dimensional N-body simulations, in order to stimulate new ideas in the
machine learning community and move closer to the practical use of generative
models in cosmology. As a first benchmark result, we propose a scalable GAN
approach for training a generator of N-body three-dimensional cubes. Our
technique relies on two key building blocks, (i) splitting the generation of
the high-dimensional data into smaller parts, and (ii) using a multi-scale
approach that efficiently captures global image features that might otherwise
be lost in the splitting process. We evaluate the performance of our model for
the generation of N-body samples using various statistical measures commonly
used in cosmology. Our results show that the proposed model produces samples of
high visual quality, although the statistical analysis reveals that capturing
rare features in the data poses significant problems for the generative models.
We make the data, quality evaluation routines, and the proposed GAN
architecture publicly available at https://github.com/nperraud/3DcosmoGAN

negative:
Graphs are important data representations for describing objects and their
relationships, which appear in a wide diversity of real-world scenarios. As one
of a critical problem in this area, graph generation considers learning the
distributions of given graphs and generating more novel graphs. Owing to their
wide range of applications, generative models for graphs, which have a rich
history, however, are traditionally hand-crafted and only capable of modeling a
few statistical properties of graphs. Recent advances in deep generative models
for graph generation is an important step towards improving the fidelity of
generated graphs and paves the way for new kinds of applications. This article
provides an extensive overview of the literature in the field of deep
generative models for graph generation. Firstly, the formal definition of deep
generative models for the graph generation and the preliminary knowledge are
provided. Secondly, taxonomies of deep generative models for both unconditional
and conditional graph generation are proposed respectively; the existing works
of each are compared and analyzed. After that, an overview of the evaluation
metrics in this specific domain is provided. Finally, the applications that
deep graph generation enables are summarized and five promising future research
directions are highlighted.

negative:
Generative Adversarial Networks (GANs), as a framework for estimating
generative models via an adversarial process, have attracted huge attention and
have proven to be powerful in a variety of tasks. However, training GANs is
well known for being delicate and unstable, partially caused by its sig- moid
cross entropy loss function for the discriminator. To overcome such a problem,
many researchers directed their attention on various ways to measure how close
the model distribution and real distribution are and have applied dif- ferent
metrics as their objective functions. In this paper, we propose a novel
framework to train GANs based on distance metric learning and we call it Metric
Learning-based Gener- ative Adversarial Network (MLGAN). The discriminator of
MLGANs can dynamically learn an appropriate metric, rather than a static one,
to measure the distance between generated samples and real samples. Afterwards,
MLGANs update the generator under the newly learned metric. We evaluate our ap-
proach on several representative datasets and the experimen- tal results
demonstrate that MLGANs can achieve superior performance compared with several
existing state-of-the-art approaches. We also empirically show that MLGANs
could increase the stability of training GANs.

negative:
Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural
language processing. Moreover, transforming an object or person to a desired
shape become a well-studied research in the GANs. GANs are powerful models for
learning complex distributions to synthesize semantically meaningful samples.
However, there is a lack of comprehensive review in this field, especially lack
of a collection of GANs loss-variant, evaluation metrics, remedies for diverse
image generation, and stable training. Given the current fast GANs development,
in this survey, we provide a comprehensive review of adversarial models for
image synthesis. We summarize the synthetic image generation methods, and
discuss the categories including image-to-image translation, fusion image
generation, label-to-image mapping, and text-to-image translation. We organize
the literature based on their base models, developed ideas related to
architectures, constraints, loss functions, evaluation metrics, and training
datasets. We present milestones of adversarial models, review an extensive
selection of previous works in various categories, and present insights on the
development route from the model-based to data-driven methods. Further, we
highlight a range of potential future research directions. One of the unique
features of this review is that all software implementations of these GAN
methods and datasets have been collected and made available in one place at
https://github.com/pshams55/GAN-Case-Study.

negative:
An open secret in contemporary machine learning is that many models work
beautifully on standard benchmarks but fail to generalize outside the lab. This
has been attributed to biased training data, which provide poor coverage over
real world events. Generative models are no exception, but recent advances in
generative adversarial networks (GANs) suggest otherwise - these models can now
synthesize strikingly realistic and diverse images. Is generative modeling of
photos a solved problem? We show that although current GANs can fit standard
datasets very well, they still fall short of being comprehensive models of the
visual manifold. In particular, we study their ability to fit simple
transformations such as camera movements and color changes. We find that the
models reflect the biases of the datasets on which they are trained (e.g.,
centered objects), but that they also exhibit some capacity for generalization:
by "steering" in latent space, we can shift the distribution while still
creating realistic images. We hypothesize that the degree of distributional
shift is related to the breadth of the training data distribution. Thus, we
conduct experiments to quantify the limits of GAN transformations and introduce
techniques to mitigate the problem. Code is released on our project page:
https://ali-design.github.io/gan_steerability/

negative:
Evaluating generative adversarial networks (GANs) is inherently challenging.
In this paper, we revisit several representative sample-based evaluation
metrics for GANs, and address the problem of how to evaluate the evaluation
metrics. We start with a few necessary conditions for metrics to produce
meaningful scores, such as distinguishing real from generated samples,
identifying mode dropping and mode collapsing, and detecting overfitting. With
a series of carefully designed experiments, we comprehensively investigate
existing sample-based metrics and identify their strengths and limitations in
practical settings. Based on these results, we observe that kernel Maximum Mean
Discrepancy (MMD) and the 1-Nearest-Neighbor (1-NN) two-sample test seem to
satisfy most of the desirable properties, provided that the distances between
samples are computed in a suitable feature space. Our experiments also unveil
interesting properties about the behavior of several popular GAN models, such
as whether they are memorizing training samples, and how far they are from
learning the target distribution.

negative:
Generative Adversarial Network (GAN) and its variants exhibit
state-of-the-art performance in the class of generative models. To capture
higher-dimensional distributions, the common learning procedure requires high
computational complexity and a large number of parameters. The problem of
employing such massive framework arises when deploying it on a platform with
limited computational power such as mobile phones. In this paper, we present a
new generative adversarial framework by representing each layer as a tensor
structure connected by multilinear operations, aiming to reduce the number of
model parameters by a large factor while preserving the generative performance
and sample quality. To learn the model, we employ an efficient algorithm which
alternatively optimizes both discriminator and generator. Experimental outcomes
demonstrate that our model can achieve high compression rate for model
parameters up to $35$ times when compared to the original GAN for MNIST
dataset.

negative:
Generative adversarial networks (GAN) have been effective for learning
generative models for real-world data. However, existing GANs (GAN and its
variants) tend to suffer from training problems such as instability and mode
collapse. In this paper, we propose a novel GAN framework called evolutionary
generative adversarial networks (E-GAN) for stable GAN training and improved
generative performance. Unlike existing GANs, which employ a pre-defined
adversarial objective function alternately training a generator and a
discriminator, we utilize different adversarial training objectives as mutation
operations and evolve a population of generators to adapt to the environment
(i.e., the discriminator). We also utilize an evaluation mechanism to measure
the quality and diversity of generated samples, such that only well-performing
generator(s) are preserved and used for further training. In this way, E-GAN
overcomes the limitations of an individual adversarial training objective and
always preserves the best offspring, contributing to progress in and the
success of GANs. Experiments on several datasets demonstrate that E-GAN
achieves convincing generative performance and reduces the training problems
inherent in existing GANs.

negative:
One of the most significant challenges in statistical signal processing and
machine learning is how to obtain a generative model that can produce samples
of large-scale data distribution, such as images and speeches. Generative
Adversarial Network (GAN) is an effective method to address this problem. The
GANs provide an appropriate way to learn deep representations without
widespread use of labeled training data. This approach has attracted the
attention of many researchers in computer vision since it can generate a large
amount of data without precise modeling of the probability density function
(PDF). In GANs, the generative model is estimated via a competitive process
where the generator and discriminator networks are trained simultaneously. The
generator learns to generate plausible data, and the discriminator learns to
distinguish fake data created by the generator from real data samples. Given
the rapid growth of GANs over the last few years and their application in
various fields, it is necessary to investigate these networks accurately. In
this paper, after introducing the main concepts and the theory of GAN, two new
deep generative models are compared, the evaluation metrics utilized in the
literature and challenges of GANs are also explained. Moreover, the most
remarkable GAN architectures are categorized and discussed. Finally, the
essential applications in computer vision are examined.

negative:
Generative adversarial networks (GANs) aim to generate realistic data from
some prior distribution (e.g., Gaussian noises). However, such prior
distribution is often independent of real data and thus may lose semantic
information (e.g., geometric structure or content in images) of data. In
practice, the semantic information might be represented by some latent
distribution learned from data, which, however, is hard to be used for sampling
in GANs. In this paper, rather than sampling from the pre-defined prior
distribution, we propose a Local Coordinate Coding (LCC) based sampling method
to improve GANs. We derive a generalization bound for LCC based GANs and prove
that a small dimensional input is sufficient to achieve good generalization.
Extensive experiments on various real-world datasets demonstrate the
effectiveness of the proposed method.

negative:
We propose a novel method to directly learn a stochastic transition operator
whose repeated application provides generated samples. Traditional undirected
graphical models approach this problem indirectly by learning a Markov chain
model whose stationary distribution obeys detailed balance with respect to a
parameterized energy function. The energy function is then modified so the
model and data distributions match, with no guarantee on the number of steps
required for the Markov chain to converge. Moreover, the detailed balance
condition is highly restrictive: energy based models corresponding to neural
networks must have symmetric weights, unlike biological neural circuits. In
contrast, we develop a method for directly learning arbitrarily parameterized
transition operators capable of expressing non-equilibrium stationary
distributions that violate detailed balance, thereby enabling us to learn more
biologically plausible asymmetric neural networks and more general non-energy
based dynamical systems. The proposed training objective, which we derive via
principled variational methods, encourages the transition operator to "walk
back" in multi-step trajectories that start at data-points, as quickly as
possible back to the original data points. We present a series of experimental
results illustrating the soundness of the proposed approach, Variational
Walkback (VW), on the MNIST, CIFAR-10, SVHN and CelebA datasets, demonstrating
superior samples compared to earlier attempts to learn a transition operator.
We also show that although each rapid training trajectory is limited to a
finite but variable number of steps, our transition operator continues to
generate good samples well past the length of such trajectories, thereby
demonstrating the match of its non-equilibrium stationary distribution to the
data distribution. Source Code: http://github.com/anirudh9119/walkback_nips17

negative:
In this paper, we propose a diffusion probabilistic model for handwriting
generation. Diffusion models are a class of generative models where samples
start from Gaussian noise and are gradually denoised to produce output. Our
method of handwriting generation does not require using any text-recognition
based, writer-style based, or adversarial loss functions, nor does it require
training of auxiliary networks. Our model is able to incorporate writer
stylistic features directly from image data, eliminating the need for user
interaction during sampling. Experiments reveal that our model is able to
generate realistic , high quality images of handwritten text in a similar style
to a given writer. Our implementation can be found at
https://github.com/tcl9876/Diffusion-Handwriting-Generation

negative:
Generative Adversarial Networks (GANs) have recently introduced effective
methods of performing Image-to-Image translations. These models can be applied
and generalized to a variety of domains in Image-to-Image translation without
changing any parameters. In this paper, we survey and analyze eight
Image-to-Image Generative Adversarial Networks: Pix2Pix, CycleGAN, CoGAN,
StarGAN, MUNIT, StarGAN2, DA-GAN, and Self Attention GAN. Each of these models
presented state-of-the-art results and introduced new techniques to build
Image-to-Image GANs. In addition to a survey of the models, we also survey the
18 datasets they were trained on and the 9 metrics they were evaluated on.
Finally, we present results of a controlled experiment for 6 of these models on
a common set of metrics and datasets. The results were mixed and showed that,
on certain datasets, tasks, and metrics, some models outperformed others. The
last section of this paper discusses those results and establishes areas of
future research. As researchers continue to innovate new Image-to-Image GANs,
it is important to gain a good understanding of the existing methods, datasets,
and metrics. This paper provides a comprehensive overview and discussion to
help build this foundation.

negative:
We present a variety of new architectural features and training procedures
that we apply to the generative adversarial networks (GANs) framework. We focus
on two applications of GANs: semi-supervised learning, and the generation of
images that humans find visually realistic. Unlike most work on generative
models, our primary goal is not to train a model that assigns high likelihood
to test data, nor do we require the model to be able to learn well without
using any labels. Using our new techniques, we achieve state-of-the-art results
in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated
images are of high quality as confirmed by a visual Turing test: our model
generates MNIST samples that humans cannot distinguish from real data, and
CIFAR-10 samples that yield a human error rate of 21.3%. We also present
ImageNet samples with unprecedented resolution and show that our methods enable
the model to learn recognizable features of ImageNet classes.

negative:
Sketch-based image retrieval (SBIR) is the task of retrieving images from a
natural image database that correspond to a given hand-drawn sketch. Ideally,
an SBIR model should learn to associate components in the sketch (say, feet,
tail, etc.) with the corresponding components in the image having similar shape
characteristics. However, current evaluation methods simply focus only on
coarse-grained evaluation where the focus is on retrieving images which belong
to the same class as the sketch but not necessarily having the same shape
characteristics as in the sketch. As a result, existing methods simply learn to
associate sketches with classes seen during training and hence fail to
generalize to unseen classes. In this paper, we propose a new benchmark for
zero-shot SBIR where the model is evaluated in novel classes that are not seen
during training. We show through extensive experiments that existing models for
SBIR that are trained in a discriminative setting learn only class specific
mappings and fail to generalize to the proposed zero-shot setting. To
circumvent this, we propose a generative approach for the SBIR task by
proposing deep conditional generative models that take the sketch as an input
and fill the missing information stochastically. Experiments on this new
benchmark created from the "Sketchy" dataset, which is a large-scale database
of sketch-photo pairs demonstrate that the performance of these generative
models is significantly better than several state-of-the-art approaches in the
proposed zero-shot framework of the coarse-grained SBIR task.

negative:
Generative neural network models, including Generative Adversarial Network
(GAN) and Auto-Encoders (AE), are among the most popular neural network models
to generate adversarial data. The GAN model is composed of a generator that
produces synthetic data and of a discriminator that discriminates between the
generator's output and the true data. AE consist of an encoder which maps the
model distribution to a latent manifold and of a decoder which maps the latent
manifold to a reconstructed distribution. However, generative models are known
to provoke chaotically scattered reconstructed distribution during their
training, and consequently, incomplete generated adversarial distributions.
Current distance measures fail to address this problem because they are not
able to acknowledge the shape of the data manifold, i.e. its topological
features, and the scale at which the manifold should be analyzed. We propose
Persistent Homology for Generative Models, PHom-GeM, a new methodology to
assess and measure the distribution of a generative model. PHom-GeM minimizes
an objective function between the true and the reconstructed distributions and
uses persistent homology, the study of the topological features of a space at
different spatial resolutions, to compare the nature of the true and the
generated distributions. Our experiments underline the potential of persistent
homology for Wasserstein GAN in comparison to Wasserstein AE and Variational
AE. The experiments are conducted on a real-world data set particularly
challenging for traditional distance measures and generative neural network
models. PHom-GeM is the first methodology to propose a topological distance
measure, the bottleneck distance, for generative models used to compare
adversarial samples in the context of credit card transactions.

negative:
Data sampling acts as a pivotal role in training deep learning models.
However, an effective sampling schedule is difficult to learn due to the
inherently high dimension of parameters in learning the sampling schedule. In
this paper, we propose an AutoSampling method to automatically learn sampling
schedules for model training, which consists of the multi-exploitation step
aiming for optimal local sampling schedules and the exploration step for the
ideal sampling distribution. More specifically, we achieve sampling schedule
search with shortened exploitation cycle to provide enough supervision. In
addition, we periodically estimate the sampling distribution from the learned
sampling schedules and perturb it to search in the distribution space. The
combination of two searches allows us to learn a robust sampling schedule. We
apply our AutoSampling method to a variety of image classification tasks
illustrating the effectiveness of the proposed method.

negative:
Generative adversarial networks (GANs) have shown tremendous promise in
learning to generate data and effective at aiding semi-supervised
classification. However, to this point, semi-supervised GAN methods make the
assumption that the unlabeled data set contains only samples of the joint
distribution of the classes of interest, referred to as inliers. Consequently,
when presented with a sample from other distributions, referred to as outliers,
GANs perform poorly at determining that it is not qualified to make a decision
on the sample. The problem of discriminating outliers from inliers while
maintaining classification accuracy is referred to here as the DOIC problem. In
this work, we describe an architecture that combines self-organizing maps
(SOMs) with SS-GANS with the goal of mitigating the DOIC problem and
experimental results indicating that the architecture achieves the goal.
Multiple experiments were conducted on hyperspectral image data sets. The
SS-GANS performed slightly better than supervised GANS on classification
problems with and without the SOM. Incorporating the SOMs into the SS-GANs and
the supervised GANS led to substantially mitigation of the DOIC problem when
compared to SS-GANS and GANs without the SOMs. Furthermore, the SS-GANS
performed much better than GANS on the DOIC problem, even without the SOMs.

negative:
We extend and improve the work of Model Agnostic Anchors for explanations on
image classification through the use of generative adversarial networks (GANs).
Using GANs, we generate samples from a more realistic perturbation
distribution, by optimizing under a lower dimensional latent space. This
increases the trust in an explanation, as results now come from images that are
more likely to be found in the original training set of a classifier, rather
than an overlay of random images. A large drawback to our method is the
computational complexity of sampling through optimization; to address this, we
implement more efficient algorithms, including a diverse encoder. Lastly, we
share results from the MNIST and CelebA datasets, and note that our
explanations can lead to smaller and higher precision anchors.

negative:
Prior domain knowledge can greatly help to learn generative models. However,
it is often too costly to hard-code prior knowledge as a specific model
architecture, so we often have to use general-purpose models. In this paper, we
propose a method to incorporate prior knowledge of feature relations into the
learning of general-purpose generative models. To this end, we formulate a
regularizer that makes the marginals of a generative model to follow prescribed
relative dependence of features. It can be incorporated into off-the-shelf
learning methods of many generative models, including variational autoencoders
and generative adversarial networks, as its gradients can be computed using
standard backpropagation techniques. We show the effectiveness of the proposed
method with experiments on multiple types of datasets and generative models.

negative:
The Generative Adversarial Network (GAN) is a state-of-the-art technique in
the field of deep learning. A number of recent papers address the theory and
applications of GANs in various fields of image processing. Fewer studies,
however, have directly evaluated GAN outputs. Those that have been conducted
focused on using classification performance, e.g., Inception Score (IS) and
statistical metrics, e.g., Fr\'echet Inception Distance (FID). Here, we
consider a fundamental way to evaluate GANs by directly analyzing the images
they generate, instead of using them as inputs to other classifiers. We
characterize the performance of a GAN as an image generator according to three
aspects: 1) Creativity: non-duplication of the real images. 2) Inheritance:
generated images should have the same style, which retains key features of the
real images. 3) Diversity: generated images are different from each other. A
GAN should not generate a few different images repeatedly. Based on the three
aspects of ideal GANs, we have designed the Likeness Score (LS) to evaluate GAN
performance, and have applied it to evaluate several typical GANs. We compared
our proposed measure with two commonly used GAN evaluation methods: IS and FID,
and four additional measures. Furthermore, we discuss how these evaluations
could help us deepen our understanding of GANs and improve their performance.

negative:
We present two new metrics for evaluating generative models in the
class-conditional image generation setting. These metrics are obtained by
generalizing the two most popular unconditional metrics: the Inception Score
(IS) and the Fre'chet Inception Distance (FID). A theoretical analysis shows
the motivation behind each proposed metric and links the novel metrics to their
unconditional counterparts. The link takes the form of a product in the case of
IS or an upper bound in the FID case. We provide an extensive empirical
evaluation, comparing the metrics to their unconditional variants and to other
metrics, and utilize them to analyze existing generative models, thus providing
additional insights about their performance, from unlearned classes to mode
collapse.

negative:
Generative Adversarial Networks (GANs) is a powerful family of models that
learn an underlying distribution to generate synthetic data. Many existing
studies of GANs focus on improving the realness of the generated image data for
visual applications, and few of them concern about improving the quality of the
generated data for training other classifiers -- a task known as the model
compatibility problem. As a consequence, existing GANs often prefer generating
`easier' synthetic data that are far from the boundaries of the classifiers,
and refrain from generating near-boundary data, which are known to play an
important roles in training the classifiers. To improve GAN in terms of model
compatibility, we propose Boundary-Calibration GANs (BCGANs), which leverage
the boundary information from a set of pre-trained classifiers using the
original data. In particular, we introduce an auxiliary Boundary-Calibration
loss (BC-loss) into the generator of GAN to match the statistics between the
posterior distributions of original data and generated data with respect to the
boundaries of the pre-trained classifiers. The BC-loss is provably unbiased and
can be easily coupled with different GAN variants to improve their model
compatibility. Experimental results demonstrate that BCGANs not only generate
realistic images like original GANs but also achieves superior model
compatibility than the original GANs.

negative:
Image generation remains a fundamental problem in artificial intelligence in
general and deep learning in specific. The generative adversarial network (GAN)
was successful in generating high quality samples of natural images. We propose
a model called composite generative adversarial network, that reveals the
complex structure of images with multiple generators in which each generator
generates some part of the image. Those parts are combined by alpha blending
process to create a new single image. It can generate, for example, background
and face sequentially with two generators, after training on face dataset.
Training was done in an unsupervised way without any labels about what each
generator should generate. We found possibilities of learning the structure by
using this generative model empirically.

negative:
Generative Adversarial Networks (GANs) became very popular for generation of
realistically looking images. In this paper, we propose to use GANs to
synthesize artificial financial data for research and benchmarking purposes. We
test this approach on three American Express datasets, and show that properly
trained GANs can replicate these datasets with high fidelity. For our
experiments, we define a novel type of GAN, and suggest methods for data
preprocessing that allow good training and testing performance of GANs. We also
discuss methods for evaluating the quality of generated data, and their
comparison with the original real data.

negative:
Generative Adversarial Networks (GANs) can accurately model complex
multi-dimensional data and generate realistic samples. However, due to their
implicit estimation of data distributions, their evaluation is a challenging
task. The majority of research efforts associated with tackling this issue were
validated by qualitative visual evaluation. Such approaches do not generalize
well beyond the image domain. Since many of those evaluation metrics are
proposed and bound to the vision domain, they are difficult to apply to other
domains. Quantitative measures are necessary to better guide the training and
comparison of different GANs models. In this work, we leverage Siamese neural
networks to propose a domain-agnostic evaluation metric: (1) with a qualitative
evaluation that is consistent with human evaluation, (2) that is robust
relative to common GAN issues such as mode dropping and invention, and (3) does
not require any pretrained classifier. The empirical results in this paper
demonstrate the superiority of this method compared to the popular Inception
Score and are competitive with the FID score.

negative:
Generative Adversarial Networks (GANs) have become a very popular tool for
implicitly learning high-dimensional probability distributions. Several
improvements have been made to the original GAN formulation to address some of
its shortcomings like mode collapse, convergence issues, entanglement, poor
visual quality etc. While a significant effort has been directed towards
improving the visual quality of images generated by GANs, it is rather
surprising that objective image quality metrics have neither been employed as
cost functions nor as regularizers in GAN objective functions. In this work, we
show how a distance metric that is a variant of the Structural SIMilarity
(SSIM) index (a popular full-reference image quality assessment algorithm), and
a novel quality aware discriminator gradient penalty function that is inspired
by the Natural Image Quality Evaluator (NIQE, a popular no-reference image
quality assessment algorithm) can each be used as excellent regularizers for
GAN objective functions. Specifically, we demonstrate state-of-the-art
performance using the Wasserstein GAN gradient penalty (WGAN-GP) framework over
CIFAR-10, STL10 and CelebA datasets.

negative:
A crucial factor to trust Machine Learning (ML) algorithm decisions is a good
representation of its application field by the training dataset. This is
particularly true when parts of the training data have been artificially
generated to overcome common training problems such as lack of data or
imbalanced dataset. Over the last few years, Generative Adversarial Networks
(GANs) have shown remarkable results in generating realistic data. However,
this ML approach lacks an objective function to evaluate the quality of the
generated data. Numerous GAN applications focus on generating image data mostly
because they can be easily evaluated by a human eye. Less efforts have been
made to generate time series data. Assessing their quality is more complicated,
particularly for technical data. In this paper, we propose a human-centered
approach supporting a ML or domain expert to accomplish this task using Visual
Analytics (VA) techniques. The presented approach consists of two views, namely
a GAN Iteration View showing similarity metrics between real and generated data
over the iterations of the generation process and a Detailed Comparative View
equipped with different time series visualizations such as TimeHistograms, to
compare the generated data at different iteration steps. Starting from the GAN
Iteration View, the user can choose suitable iteration steps for detailed
inspection. We evaluate our approach with a usage scenario that enabled an
efficient comparison of two different GAN models.

negative:
We present a novel method and analysis to train generative adversarial
networks (GAN) in a stable manner. As shown in recent analysis, training is
often undermined by the probability distribution of the data being zero on
neighborhoods of the data space. We notice that the distributions of real and
generated data should match even when they undergo the same filtering.
Therefore, to address the limited support problem we propose to train GANs by
using different filtered versions of the real and generated data distributions.
In this way, filtering does not prevent the exact matching of the data
distribution, while helping training by extending the support of both
distributions. As filtering we consider adding samples from an arbitrary
distribution to the data, which corresponds to a convolution of the data
distribution with the arbitrary one. We also propose to learn the generation of
these samples so as to challenge the discriminator in the adversarial training.
We show that our approach results in a stable and well-behaved training of even
the original minimax GAN formulation. Moreover, our technique can be
incorporated in most modern GAN formulations and leads to a consistent
improvement on several common datasets.

negative:
Generative models estimate the underlying distribution of a dataset to
generate realistic samples according to that distribution. In this paper, we
present the first membership inference attacks against generative models: given
a data point, the adversary determines whether or not it was used to train the
model. Our attacks leverage Generative Adversarial Networks (GANs), which
combine a discriminative and a generative model, to detect overfitting and
recognize inputs that were part of training datasets, using the discriminator's
capacity to learn statistical differences in distributions.
  We present attacks based on both white-box and black-box access to the target
model, against several state-of-the-art generative models, over datasets of
complex representations of faces (LFW), objects (CIFAR-10), and medical images
(Diabetic Retinopathy). We also discuss the sensitivity of the attacks to
different training parameters, and their robustness against mitigation
strategies, finding that defenses are either ineffective or lead to
significantly worse performances of the generative models in terms of training
stability and/or sample quality.

negative:
Generative adversarial networks (GANs) typically require ample data for
training in order to synthesize high-fidelity images. Recent studies have shown
that training GANs with limited data remains formidable due to discriminator
overfitting, the underlying cause that impedes the generator's convergence.
This paper introduces a novel strategy called Adaptive Pseudo Augmentation
(APA) to encourage healthy competition between the generator and the
discriminator. As an alternative method to existing approaches that rely on
standard data augmentations or model regularization, APA alleviates overfitting
by employing the generator itself to augment the real data distribution with
generated images, which deceives the discriminator adaptively. Extensive
experiments demonstrate the effectiveness of APA in improving synthesis quality
in the low-data regime. We provide a theoretical analysis to examine the
convergence and rationality of our new training strategy. APA is simple and
effective. It can be added seamlessly to powerful contemporary GANs, such as
StyleGAN2, with negligible computational cost.

negative:
Generative Adversarial Networks (GANs) have achieved a great success in
unsupervised learning. Despite its remarkable empirical performance, there are
limited theoretical studies on the statistical properties of GANs. This paper
provides approximation and statistical guarantees of GANs for the estimation of
data distributions that have densities in a H\"{o}lder space. Our main result
shows that, if the generator and discriminator network architectures are
properly chosen, GANs are consistent estimators of data distributions under
strong discrepancy metrics, such as the Wasserstein-1 distance. Furthermore,
when the data distribution exhibits low-dimensional structures, we show that
GANs are capable of capturing the unknown low-dimensional structures in data
and enjoy a fast statistical convergence, which is free of curse of the ambient
dimensionality. Our analysis for low-dimensional data builds upon a universal
approximation theory of neural networks with Lipschitz continuity guarantees,
which may be of independent interest.

negative:
Generative modeling has recently seen many exciting developments with the
advent of deep generative architectures such as Variational Auto-Encoders (VAE)
or Generative Adversarial Networks (GAN). The ability to draw synthetic i.i.d.
observations with the same joint probability distribution as a given dataset
has a wide range of applications including representation learning, compression
or imputation. It appears that it also has many applications in privacy
preserving data analysis, especially when used in conjunction with differential
privacy techniques. This paper focuses on synthetic data generation models with
privacy preserving applications in mind. It introduces a novel architecture,
the Composable Generative Model (CGM) that is state-of-the-art in tabular data
generation. Any conditional generative model can be used as a sub-component of
the CGM, including CGMs themselves, allowing the generation of numerical,
categorical data as well as images, text, or time series. The CGM has been
evaluated on 13 datasets (6 standard datasets and 7 simulated) and compared to
14 recent generative models. It beats the state of the art in tabular data
generation by a significant margin.

negative:
Generative Adversarial Networks (GANs) have been used to model the underlying
probability distribution of sample based datasets. GANs are notoriuos for
training difficulties and their dependence on arbitrary hyperparameters. One
recent improvement in GAN literature is to use the Wasserstein distance as loss
function leading to Wasserstein Generative Adversarial Networks (WGANs). Using
this as a basis, we show various ways in which the Wasserstein distance is
estimated for the task of generative modelling. Additionally, the secrets in
training such models are shown and summarized at the end of this work. Where
applicable, we extend current works to different algorithms, different cost
functions, and different regularization schemes to improve generative models.

negative:
A primary difficulty with unsupervised discovery of structure in large data
sets is a lack of quantitative evaluation criteria. In this work, we propose
and investigate several metrics for evaluating and comparing generative models
of behavior learned using imitation learning. Compared to the commonly-used
model log-likelihood, these criteria look at longer temporal relationships in
behavior, are relevant if behavior has some properties that are inherently
unpredictable, and highlight biases in the overall distribution of behaviors
produced by the model. Pointwise metrics compare real to model-predicted
trajectories given true past information. Distribution metrics compare
statistics of the model simulating behavior in open loop, and are inspired by
how experimental biologists evaluate the effects of manipulations on animal
behavior. We show that the proposed metrics correspond with biologists'
intuitions about behavior, and allow us to evaluate models, understand their
biases, and enable us to propose new research directions.

negative:
Auto-encoding generative adversarial networks (GANs) combine the standard GAN
algorithm, which discriminates between real and model-generated data, with a
reconstruction loss given by an auto-encoder. Such models aim to prevent mode
collapse in the learned generative model by ensuring that it is grounded in all
the available training data. In this paper, we develop a principle upon which
auto-encoders can be combined with generative adversarial networks by
exploiting the hierarchical structure of the generative model. The underlying
principle shows that variational inference can be used a basic tool for
learning, but with the in- tractable likelihood replaced by a synthetic
likelihood, and the unknown posterior distribution replaced by an implicit
distribution; both synthetic likelihoods and implicit posterior distributions
can be learned using discriminators. This allows us to develop a natural fusion
of variational auto-encoders and generative adversarial networks, combining the
best of both these methods. We describe a unified objective for optimization,
discuss the constraints needed to guide learning, connect to the wide range of
existing work, and use a battery of tests to systematically and quantitatively
assess the performance of our method.

negative:
We study how to generate molecule conformations (i.e., 3D structures) from a
molecular graph. Traditional methods, such as molecular dynamics, sample
conformations via computationally expensive simulations. Recently, machine
learning methods have shown great potential by training on a large collection
of conformation data. Challenges arise from the limited model capacity for
capturing complex distributions of conformations and the difficulty in modeling
long-range dependencies between atoms. Inspired by the recent progress in deep
generative models, in this paper, we propose a novel probabilistic framework to
generate valid and diverse conformations given a molecular graph. We propose a
method combining the advantages of both flow-based and energy-based models,
enjoying: (1) a high model capacity to estimate the multimodal conformation
distribution; (2) explicitly capturing the complex long-range dependencies
between atoms in the observation space. Extensive experiments demonstrate the
superior performance of the proposed method on several benchmarks, including
conformation generation and distance modeling tasks, with a significant
improvement over existing generative models for molecular conformation
sampling.

negative:
Generative adversarial networks (GAN) have shown remarkable results in image
generation tasks. High fidelity class-conditional GAN methods often rely on
stabilization techniques by constraining the global Lipschitz continuity. Such
regularization leads to less expressive models and slower convergence speed;
other techniques, such as the large batch training, require unconventional
computing power and are not widely accessible. In this paper, we develop an
efficient algorithm, namely FastGAN (Free AdverSarial Training), to improve the
speed and quality of GAN training based on the adversarial training technique.
We benchmark our method on CIFAR10, a subset of ImageNet, and the full ImageNet
datasets. We choose strong baselines such as SNGAN and SAGAN; the results
demonstrate that our training algorithm can achieve better generation quality
(in terms of the Inception score and Frechet Inception distance) with less
overall training time. Most notably, our training algorithm brings ImageNet
training to the broader public by requiring 2-4 GPUs.

negative:
An interpretable generative model for handwritten digits synthesis is
proposed in this work. Modern image generative models, such as Generative
Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are trained by
backpropagation (BP). The training process is complex and the underlying
mechanism is difficult to explain. We propose an interpretable multi-stage PCA
method to achieve the same goal and use handwritten digit images synthesis as
an illustrative example. First, we derive principal-component-analysis-based
(PCA-based) transform kernels at each stage based on the covariance of its
inputs. This results in a sequence of transforms that convert input images of
correlated pixels to spectral vectors of uncorrelated components. In other
words, it is a whitening process. Then, we can synthesize an image based on
random vectors and multi-stage transform kernels through a coloring process.
The generative model is a feedforward (FF) design since no BP is used in model
parameter determination. Its design complexity is significantly lower, and the
whole design process is explainable. Finally, we design an FF generative model
using the MNIST dataset, compare synthesis results with those obtained by
state-of-the-art GAN and VAE methods, and show that the proposed generative
model achieves comparable performance.

negative:
In this work, we develop an importance sampling estimator by coupling the
reduced-order model and the generative model in a problem setting of
uncertainty quantification. The target is to estimate the probability that the
quantity of interest (QoI) in a complex system is beyond a given threshold. To
avoid the prohibitive cost of sampling a large scale system, the reduced-order
model is usually considered for a trade-off between efficiency and accuracy.
However, the Monte Carlo estimator given by the reduced-order model is biased
due to the error from dimension reduction. To correct the bias, we still need
to sample the fine model. An effective technique to reduce the variance
reduction is importance sampling, where we employ the generative model to
estimate the distribution of the data from the reduced-order model and use it
for the change of measure in the importance sampling estimator. To compensate
the approximation errors of the reduced-order model, more data that induce a
slightly smaller QoI than the threshold need to be included into the training
set. Although the amount of these data can be controlled by a posterior error
estimate, redundant data, which may outnumber the effective data, will be kept
due to the epistemic uncertainty. To deal with this issue, we introduce a
weighted empirical distribution to process the data from the reduced-order
model. The generative model is then trained by minimizing the cross entropy
between it and the weighted empirical distribution. We also introduce a penalty
term into the objective function to deal with the overfitting for more
robustness. Numerical results are presented to demonstrate the effectiveness of
the proposed methodology.

negative:
The new wave of successful generative models in machine learning has
increased the interest in deep learning driven de novo drug design. However,
assessing the performance of such generative models is notoriously difficult.
Metrics that are typically used to assess the performance of such generative
models are the percentage of chemically valid molecules or the similarity to
real molecules in terms of particular descriptors, such as the partition
coefficient (logP) or druglikeness. However, method comparison is difficult
because of the inconsistent use of evaluation metrics, the necessity for
multiple metrics, and the fact that some of these measures can easily be
tricked by simple rule-based systems. We propose a novel distance measure
between two sets of molecules, called Fr\'echet ChemNet distance (FCD), that
can be used as an evaluation metric for generative models. The FCD is similar
to a recently established performance metric for comparing image generation
methods, the Fr\'echet Inception Distance (FID). Whereas the FID uses one of
the hidden layers of InceptionNet, the FCD utilizes the penultimate layer of a
deep neural network called ChemNet, which was trained to predict drug
activities. Thus, the FCD metric takes into account chemically and biologically
relevant information about molecules, and also measures the diversity of the
set via the distribution of generated molecules. The FCD's advantage over
previous metrics is that it can detect if generated molecules are a) diverse
and have similar b) chemical and c) biological properties as real molecules. We
further provide an easy-to-use implementation that only requires the SMILES
representation of the generated molecules as input to calculate the FCD.
Implementations are available at: https://www.github.com/bioinf-jku/FCD

negative:
The discriminative approach to classification using deep neural networks has
become the de-facto standard in various fields. Complementing recent
reservations about safety against adversarial examples, we show that
conventional discriminative methods can easily be fooled to provide incorrect
labels with very high confidence to out of distribution examples. We posit that
a generative approach is the natural remedy for this problem, and propose a
method for classification using generative models. At training time, we learn a
generative model for each class, while at test time, given an example to
classify, we query each generator for its most similar generation, and select
the class corresponding to the most similar one. Our approach is general and
can be used with expressive models such as GANs and VAEs. At test time, our
method accurately "knows when it does not know," and provides resilience to out
of distribution examples while maintaining competitive performance for standard
examples.

negative:
Real networks exhibit nontrivial topological features such as heavy-tailed
degree distribution, high clustering, and small-worldness. Researchers have
developed several generative models for synthesizing artificial networks that
are structurally similar to real networks. An important research problem is to
identify the generative model that best fits to a target network. In this
paper, we investigate this problem and our goal is to select the model that is
able to generate graphs similar to a given network instance. By the means of
generating synthetic networks with seven outstanding generative models, we have
utilized machine learning methods to develop a decision tree for model
selection. Our proposed method, which is named "Generative Model Selection for
Complex Networks" (GMSCN), outperforms existing methods with respect to
accuracy, scalability and size-independence.

negative:
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.

negative:
Recently, generative adversarial networks have gained a lot of popularity for
image generation tasks. However, such models are associated with complex
learning mechanisms and demand very large relevant datasets. This work borrows
concepts from image and video captioning models to form an image generative
framework. The model is trained in a similar fashion as recurrent captioning
model and uses the learned weights for image generation. This is done in an
inverse direction, where the input is a caption and the output is an image. The
vector representation of the sentence and frames are extracted from an
encoder-decoder model which is initially trained on similar sentence and image
pairs. Our model conditions image generation on a natural language caption. We
leverage a sequence-to-sequence model to generate synthetic captions that have
the same meaning for having a robust image generation. One key advantage of our
method is that the traditional image captioning datasets can be used for
synthetic sentence paraphrases. Results indicate that images generated through
multiple captions are better at capturing the semantic meaning of the family of
captions.

negative:
Image captioning is an ambiguous problem, with many suitable captions for an
image. To address ambiguity, beam search is the de facto method for sampling
multiple captions. However, beam search is computationally expensive and known
to produce generic captions. To address this concern, some variational
auto-encoder (VAE) and generative adversarial net (GAN) based methods have been
proposed. Though diverse, GAN and VAE are less accurate. In this paper, we
first predict a meaningful summary of the image, then generate the caption
based on that summary. We use part-of-speech as summaries, since our summary
should drive caption generation. We achieve the trifecta: (1) High accuracy for
the diverse captions as evaluated by standard captioning metrics and user
studies; (2) Faster computation of diverse captions compared to beam search and
diverse beam search; and (3) High diversity as evaluated by counting novel
sentences, distinct n-grams and mutual overlap (i.e., mBleu-4) scores.

negative:
Generative neural samplers are probabilistic models that implement sampling
using feedforward neural networks: they take a random input vector and produce
a sample from a probability distribution defined by the network weights. These
models are expressive and allow efficient computation of samples and
derivatives, but cannot be used for computing likelihoods or for
marginalization. The generative-adversarial training method allows to train
such models through the use of an auxiliary discriminative neural network. We
show that the generative-adversarial approach is a special case of an existing
more general variational divergence estimation approach. We show that any
f-divergence can be used for training generative neural samplers. We discuss
the benefits of various choices of divergence functions on training complexity
and the quality of the obtained generative models.

negative:
Generative Adversarial Network (GAN) and its variants have recently attracted
intensive research interests due to their elegant theoretical foundation and
excellent empirical performance as generative models. These tools provide a
promising direction in the studies where data availability is limited. One
common issue in GANs is that the density of the learned generative distribution
could concentrate on the training data points, meaning that they can easily
remember training samples due to the high model complexity of deep networks.
This becomes a major concern when GANs are applied to private or sensitive data
such as patient medical records, and the concentration of distribution may
divulge critical patient information. To address this issue, in this paper we
propose a differentially private GAN (DPGAN) model, in which we achieve
differential privacy in GANs by adding carefully designed noise to gradients
during the learning procedure. We provide rigorous proof for the privacy
guarantee, as well as comprehensive empirical evidence to support our analysis,
where we demonstrate that our method can generate high quality data points at a
reasonable privacy level.

negative:
We introduce a method to stabilize Generative Adversarial Networks (GANs) by
defining the generator objective with respect to an unrolled optimization of
the discriminator. This allows training to be adjusted between using the
optimal discriminator in the generator's objective, which is ideal but
infeasible in practice, and using the current value of the discriminator, which
is often unstable and leads to poor solutions. We show how this technique
solves the common problem of mode collapse, stabilizes training of GANs with
complex recurrent generators, and increases diversity and coverage of the data
distribution by the generator.

negative:
Generative Adversarial Networks (GANs) have shown remarkable results in
modeling complex distributions, but their evaluation remains an unsettled
issue. Evaluations are essential for: (i) relative assessment of different
models and (ii) monitoring the progress of a single model throughout training.
The latter cannot be determined by simply inspecting the generator and
discriminator loss curves as they behave non-intuitively. We leverage the
notion of duality gap from game theory to propose a measure that addresses both
(i) and (ii) at a low computational cost. Extensive experiments show the
effectiveness of this measure to rank different GAN models and capture the
typical GAN failure scenarios, including mode collapse and non-convergent
behaviours. This evaluation metric also provides meaningful monitoring on the
progression of the loss during training. It highly correlates with FID on
natural image datasets, and with domain specific scores for text, sound and
cosmology data where FID is not directly suitable. In particular, our proposed
metric requires no labels or a pretrained classifier, making it domain
agnostic.

negative:
Generative models, in particular generative adversarial networks (GANs), have
received significant attention recently. A number of GAN variants have been
proposed and have been utilized in many applications. Despite large strides in
terms of theoretical progress, evaluating and comparing GANs remains a daunting
task. While several measures have been introduced, as of yet, there is no
consensus as to which measure best captures strengths and limitations of models
and should be used for fair model comparison. As in other areas of computer
vision and machine learning, it is critical to settle on one or few good
measures to steer the progress in this field. In this paper, I review and
critically discuss more than 24 quantitative and 5 qualitative measures for
evaluating generative models with a particular emphasis on GAN-derived models.
I also provide a set of 7 desiderata followed by an evaluation of whether a
given measure or a family of measures is compatible with them.

negative:
Limited data access is a longstanding barrier to data-driven research and
development in the networked systems community. In this work, we explore if and
how generative adversarial networks (GANs) can be used to incentivize data
sharing by enabling a generic framework for sharing synthetic datasets with
minimal expert knowledge. As a specific target, our focus in this paper is on
time series datasets with metadata (e.g., packet loss rate measurements with
corresponding ISPs). We identify key challenges of existing GAN approaches for
such workloads with respect to fidelity (e.g., long-term dependencies, complex
multidimensional relationships, mode collapse) and privacy (i.e., existing
guarantees are poorly understood and can sacrifice fidelity). To improve
fidelity, we design a custom workflow called DoppelGANger (DG) and demonstrate
that across diverse real-world datasets (e.g., bandwidth measurements, cluster
requests, web sessions) and use cases (e.g., structural characterization,
predictive modeling, algorithm comparison), DG achieves up to 43% better
fidelity than baseline models. Although we do not resolve the privacy problem
in this work, we identify fundamental challenges with both classical notions of
privacy and recent advances to improve the privacy properties of GANs, and
suggest a potential roadmap for addressing these challenges. By shedding light
on the promise and challenges, we hope our work can rekindle the conversation
on workflows for data sharing.

negative:
A basic, and still largely unanswered, question in the context of Generative
Adversarial Networks (GANs) is whether they are truly able to capture all the
fundamental characteristics of the distributions they are trained on. In
particular, evaluating the diversity of GAN distributions is challenging and
existing methods provide only a partial understanding of this issue. In this
paper, we develop quantitative and scalable tools for assessing the diversity
of GAN distributions. Specifically, we take a classification-based perspective
and view loss of diversity as a form of covariate shift introduced by GANs. We
examine two specific forms of such shift: mode collapse and boundary
distortion. In contrast to prior work, our methods need only minimal human
supervision and can be readily applied to state-of-the-art GANs on large,
canonical datasets. Examining popular GANs using our tools indicates that these
GANs have significant problems in reproducing the more distributional
properties of their training dataset.

negative:
Training and using modern neural-network based latent-variable generative
models (like Variational Autoencoders) often require simultaneously training a
generative direction along with an inferential(encoding) direction, which
approximates the posterior distribution over the latent variables. Thus, the
question arises: how complex does the inferential model need to be, in order to
be able to accurately model the posterior distribution of a given generative
model?
  In this paper, we identify an important property of the generative map
impacting the required size of the encoder. We show that if the generative map
is "strongly invertible" (in a sense we suitably formalize), the inferential
model need not be much more complex. Conversely, we prove that there exist
non-invertible generative maps, for which the encoding direction needs to be
exponentially larger (under standard assumptions in computational complexity).
Importantly, we do not require the generative model to be layerwise invertible,
which a lot of the related literature assumes and isn't satisfied by many
architectures used in practice (e.g. convolution and pooling based networks).
Thus, we provide theoretical support for the empirical wisdom that learning
deep generative models is harder when data lies on a low-dimensional manifold.

negative:
Deep generative models are powerful tools that have produced impressive
results in recent years. These advances have been for the most part empirically
driven, making it essential that we use high quality evaluation metrics. In
this paper, we provide new insights into the Inception Score, a recently
proposed and widely used evaluation metric for generative models, and
demonstrate that it fails to provide useful guidance when comparing models. We
discuss both suboptimalities of the metric itself and issues with its
application. Finally, we call for researchers to be more systematic and careful
when evaluating and comparing generative models, as the advancement of the
field depends upon it.

negative:
Recent studies have shown that text-to-speech synthesis quality can be
improved by using glottal vocoding. This refers to vocoders that parameterize
speech into two parts, the glottal excitation and vocal tract, that occur in
the human speech production apparatus. Current glottal vocoders generate the
glottal excitation waveform by using deep neural networks (DNNs). However, the
squared error-based training of the present glottal excitation models is
limited to generating conditional average waveforms, which fails to capture the
stochastic variation of the waveforms. As a result, shaped noise is added as
post-processing. In this study, we propose a new method for predicting glottal
waveforms by generative adversarial networks (GANs). GANs are generative models
that aim to embed the data distribution in a latent space, enabling generation
of new instances very similar to the original by randomly sampling the latent
distribution. The glottal pulses generated by GANs show a stochastic component
similar to natural glottal pulses. In our experiments, we compare synthetic
speech generated using glottal waveforms produced by both DNNs and GANs. The
results show that the newly proposed GANs achieve synthesis quality comparable
to that of widely-used DNNs, without using an additive noise component.

negative:
We propose a novel architecture which is able to automatically anonymize
faces in images while retaining the original data distribution. We ensure total
anonymization of all faces in an image by generating images exclusively on
privacy-safe information. Our model is based on a conditional generative
adversarial network, generating images considering the original pose and image
background. The conditional information enables us to generate highly realistic
faces with a seamless transition between the generated face and the existing
background. Furthermore, we introduce a diverse dataset of human faces,
including unconventional poses, occluded faces, and a vast variability in
backgrounds. Finally, we present experimental results reflecting the capability
of our model to anonymize images while preserving the data distribution, making
the data suitable for further training of deep learning models. As far as we
know, no other solution has been proposed that guarantees the anonymization of
faces while generating realistic images.

negative:
Generative Adversarial Networks (GANs) are now capable of producing synthetic
face images of exceptionally high visual quality. In parallel to the
development of GANs themselves, efforts have been made to develop metrics to
objectively assess the characteristics of the synthetic images, mainly focusing
on visual quality and the variety of images. Little work has been done,
however, to assess overfitting of GANs and their ability to generate new
identities. In this paper we apply a state of the art biometric network to
various datasets of synthetic images and perform a thorough assessment of their
identity-related characteristics. We conclude that GANs can indeed be used to
generate new, imagined identities meaning that applications such as
anonymisation of image sets and augmentation of training datasets with
distractor images are viable applications. We also assess the ability of GANs
to disentangle identity from other image characteristics and propose a novel
GAN triplet loss that we show to improve this disentanglement.

negative:
This paper studies a training method to jointly estimate an energy-based
model and a flow-based model, in which the two models are iteratively updated
based on a shared adversarial value function. This joint training method has
the following traits. (1) The update of the energy-based model is based on
noise contrastive estimation, with the flow model serving as a strong noise
distribution. (2) The update of the flow model approximately minimizes the
Jensen-Shannon divergence between the flow model and the data distribution. (3)
Unlike generative adversarial networks (GAN) which estimates an implicit
probability distribution defined by a generator model, our method estimates two
explicit probabilistic distributions on the data. Using the proposed method we
demonstrate a significant improvement on the synthesis quality of the flow
model, and show the effectiveness of unsupervised feature learning by the
learned energy-based model. Furthermore, the proposed training method can be
easily adapted to semi-supervised learning. We achieve competitive results to
the state-of-the-art semi-supervised learning methods.

negative:
Class imbalance is a substantial challenge in classifying many real-world
cases. Synthetic over-sampling methods have been effective to improve the
performance of classifiers for imbalance problems. However, most synthetic
over-sampling methods generate non-diverse synthetic instances within the
convex hull formed by the existing minority instances as they only concentrate
on the minority class and ignore the vast information provided by the majority
class. They also often do not perform well for extremely imbalanced data as the
fewer the minority instances, the less information to generate synthetic
instances. Moreover, existing methods that generate synthetic instances using
the majority class distributional information cannot perform effectively when
the majority class has a multi-modal distribution. We propose a new method to
generate diverse and adaptable synthetic instances using Synthetic
Over-sampling with Minority and Majority classes (SOMM). SOMM generates
synthetic instances diversely within the minority data space. It updates the
generated instances adaptively to the neighbourhood including both classes.
Thus, SOMM performs well for both binary and multiclass imbalance problems. We
examine the performance of SOMM for binary and multiclass problems using
benchmark data sets for different imbalance levels. The empirical results show
the superiority of SOMM compared to other existing methods.

negative:
Generative models are increasingly able to produce remarkably high quality
images and text. The community has developed numerous evaluation metrics for
comparing generative models. However, these metrics do not effectively quantify
data diversity. We develop a new diversity metric that can readily be applied
to data, both synthetic and natural, of any type. Our method employs random
network distillation, a technique introduced in reinforcement learning. We
validate and deploy this metric on both images and text. We further explore
diversity in few-shot image generation, a setting which was previously
difficult to evaluate.

negative:
Recently, transformation-based self-supervised learning has been applied to
generative adversarial networks (GANs) to mitigate catastrophic forgetting in
the discriminator by introducing a stationary learning environment. However,
the separate self-supervised tasks in existing self-supervised GANs cause a
goal inconsistent with generative modeling due to the fact that their
self-supervised classifiers are agnostic to the generator distribution. To
address this problem, we propose a novel self-supervised GAN that unifies the
GAN task with the self-supervised task by augmenting the GAN labels (real or
fake) via self-supervision of data transformation. Specifically, the original
discriminator and self-supervised classifier are unified into a label-augmented
discriminator that predicts the augmented labels to be aware of both the
generator distribution and the data distribution under every transformation,
and then provide the discrepancy between them to optimize the generator.
Theoretically, we prove that the optimal generator could converge to replicate
the real data distribution. Empirically, we show that the proposed method
significantly outperforms previous self-supervised and data augmentation GANs
on both generative modeling and representation learning across benchmark
datasets.

negative:
Generative Adversarial Networks (GANs) have made great progress in
synthesizing realistic images in recent years. However, they are often trained
on image datasets with either too few samples or too many classes belonging to
different data distributions. Consequently, GANs are prone to underfitting or
overfitting, making the analysis of them difficult and constrained. Therefore,
in order to conduct a thorough study on GANs while obviating unnecessary
interferences introduced by the datasets, we train them on artificial datasets
where there are infinitely many samples and the real data distributions are
simple, high-dimensional and have structured manifolds. Moreover, the
generators are designed such that optimal sets of parameters exist.
Empirically, we find that under various distance measures, the generator fails
to learn such parameters with the GAN training procedure. We also find that
training mixtures of GANs leads to more performance gain compared to increasing
the network depth or width when the model complexity is high enough. Our
experimental results demonstrate that a mixture of generators can discover
different modes or different classes automatically in an unsupervised setting,
which we attribute to the distribution of the generation and discrimination
tasks across multiple generators and discriminators. As an example of the
generalizability of our conclusions to realistic datasets, we train a mixture
of GANs on the CIFAR-10 dataset and our method significantly outperforms the
state-of-the-art in terms of popular metrics, i.e., Inception Score (IS) and
Fr\'echet Inception Distance (FID).

negative:
Probabilistic generative models can be used for compression, denoising,
inpainting, texture synthesis, semi-supervised learning, unsupervised feature
learning, and other tasks. Given this wide range of applications, it is not
surprising that a lot of heterogeneity exists in the way these models are
formulated, trained, and evaluated. As a consequence, direct comparison between
models is often difficult. This article reviews mostly known but often
underappreciated properties relating to the evaluation and interpretation of
generative models with a focus on image models. In particular, we show that
three of the currently most commonly used criteria---average log-likelihood,
Parzen window estimates, and visual fidelity of samples---are largely
independent of each other when the data is high-dimensional. Good performance
with respect to one criterion therefore need not imply good performance with
respect to the other criteria. Our results show that extrapolation from one
criterion to another is not warranted and generative models need to be
evaluated directly with respect to the application(s) they were intended for.
In addition, we provide examples demonstrating that Parzen window estimates
should generally be avoided.

negative:
In terms of Generative Adversarial Networks (GANs), the information metric to
discriminate the generative data from the real data, lies in the key point of
generation efficiency, which plays an important role in GAN-based applications,
especially in anomaly detection. As for the original GAN, there exist drawbacks
for its hidden information measure based on KL divergence on rare events
generation and training performance for adversarial networks. Therefore, it is
significant to investigate the metrics used in GANs to improve the generation
ability as well as bring gains in the training process. In this paper, we adopt
the exponential form, referred from the information measure, i.e. MIM, to
replace the logarithm form of the original GAN. This approach is called
MIM-based GAN, has better performance on networks training and rare events
generation. Specifically, we first discuss the characteristics of training
process in this approach. Moreover, we also analyze its advantages on
generating rare events in theory. In addition, we do simulations on the
datasets of MNIST and ODDS to see that the MIM-based GAN achieves
state-of-the-art performance on anomaly detection compared with some classical
GANs.

negative:
Generative adversarial networks (GANs) are an exciting alternative to
algorithms for solving density estimation problems---using data to assess how
likely samples are to be drawn from the same distribution. Instead of
explicitly computing these probabilities, GANs learn a generator that can match
the given probabilistic source. This paper looks particularly at this matching
capability in the context of problems with one-dimensional outputs. We identify
a class of function decompositions with properties that make them well suited
to the critic role in a leading approach to GANs known as Wasserstein GANs. We
show that Taylor and Fourier series decompositions belong to our class, provide
examples of these critics outperforming standard GAN approaches, and suggest
how they can be scaled to higher dimensional problems in the future.

negative:
Generative Adversarial Networks (GANs) have been used in several machine
learning tasks such as domain transfer, super resolution, and synthetic data
generation. State-of-the-art GANs often use tens of millions of parameters,
making them expensive to deploy for applications in low SWAP (size, weight, and
power) hardware, such as mobile devices, and for applications with real time
capabilities. There has been no work found to reduce the number of parameters
used in GANs. Therefore, we propose a method to compress GANs using knowledge
distillation techniques, in which a smaller "student" GAN learns to mimic a
larger "teacher" GAN. We show that the distillation methods used on MNIST,
CIFAR-10, and Celeb-A datasets can compress teacher GANs at ratios of 1669:1,
58:1, and 87:1, respectively, while retaining the quality of the generated
image. From our experiments, we observe a qualitative limit for GAN's
compression. Moreover, we observe that, with a fixed parameter budget,
compressed GANs outperform GANs trained using standard training methods. We
conjecture that this is partially owing to the optimization landscape of
over-parameterized GANs which allows efficient training using alternating
gradient descent. Thus, training an over-parameterized GAN followed by our
proposed compression scheme provides a high quality generative model with a
small number of parameters.

negative:
Recent work has shown that Neural Ordinary Differential Equations (ODEs) can
serve as generative models of images using the perspective of Continuous
Normalizing Flows (CNFs). Such models offer exact likelihood calculation, and
invertible generation/density estimation. In this work we introduce a
Multi-Resolution variant of such models (MRCNF), by characterizing the
conditional distribution over the additional information required to generate a
fine image that is consistent with the coarse image. We introduce a
transformation between resolutions that allows for no change in the log
likelihood. We show that this approach yields comparable likelihood values for
various image datasets, with improved performance at higher resolutions, with
fewer parameters, using only 1 GPU. Further, we examine the out-of-distribution
properties of (Multi-Resolution) Continuous Normalizing Flows, and find that
they are similar to those of other likelihood-based generative models.

negative:
Creating noise from data is easy; creating data from noise is generative
modeling. We present a stochastic differential equation (SDE) that smoothly
transforms a complex data distribution to a known prior distribution by slowly
injecting noise, and a corresponding reverse-time SDE that transforms the prior
distribution back into the data distribution by slowly removing the noise.
Crucially, the reverse-time SDE depends only on the time-dependent gradient
field (\aka, score) of the perturbed data distribution. By leveraging advances
in score-based generative modeling, we can accurately estimate these scores
with neural networks, and use numerical SDE solvers to generate samples. We
show that this framework encapsulates previous approaches in score-based
generative modeling and diffusion probabilistic modeling, allowing for new
sampling procedures and new modeling capabilities. In particular, we introduce
a predictor-corrector framework to correct errors in the evolution of the
discretized reverse-time SDE. We also derive an equivalent neural ODE that
samples from the same distribution as the SDE, but additionally enables exact
likelihood computation, and improved sampling efficiency. In addition, we
provide a new way to solve inverse problems with score-based models, as
demonstrated with experiments on class-conditional generation, image
inpainting, and colorization. Combined with multiple architectural
improvements, we achieve record-breaking performance for unconditional image
generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a
competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity
generation of 1024 x 1024 images for the first time from a score-based
generative model.

negative:
Generative adversarial networks (GANs) are increasingly attracting attention
in the computer vision, natural language processing, speech synthesis and
similar domains. Arguably the most striking results have been in the area of
image synthesis. However, evaluating the performance of GANs is still an open
and challenging problem. Existing evaluation metrics primarily measure the
dissimilarity between real and generated images using automated statistical
methods. They often require large sample sizes for evaluation and do not
directly reflect human perception of image quality. In this work, we describe
an evaluation metric we call Neuroscore, for evaluating the performance of
GANs, that more directly reflects psychoperceptual image quality through the
utilization of brain signals. Our results show that Neuroscore has superior
performance to the current evaluation metrics in that: (1) It is more
consistent with human judgment; (2) The evaluation process needs much smaller
numbers of samples; and (3) It is able to rank the quality of images on a per
GAN basis. A convolutional neural network (CNN) based neuro-AI interface is
proposed to predict Neuroscore from GAN-generated images directly without the
need for neural responses. Importantly, we show that including neural responses
during the training phase of the network can significantly improve the
prediction capability of the proposed model. Materials related to this work are
provided at https://github.com/villawang/Neuro-AI-Interface.

negative:
Generative adversarial networks (GANs) provide a way to learn deep
representations without extensively annotated training data. They achieve this
through deriving backpropagation signals through a competitive process
involving a pair of networks. The representations that can be learned by GANs
may be used in a variety of applications, including image synthesis, semantic
image editing, style transfer, image super-resolution and classification. The
aim of this review paper is to provide an overview of GANs for the signal
processing community, drawing on familiar analogies and concepts where
possible. In addition to identifying different methods for training and
constructing GANs, we also point to remaining challenges in their theory and
application.

negative:
In this paper, we investigate the effect of machine learning based
anonymization on anomalous subgroup preservation. In particular, we train a
binary classifier to discover the most anomalous subgroup in a dataset by
maximizing the bias between the group's predicted odds ratio from the model and
observed odds ratio from the data. We then perform anonymization using a
variational autoencoder (VAE) to synthesize an entirely new dataset that would
ideally be drawn from the distribution of the original data. We repeat the
anomalous subgroup discovery task on the new data and compare it to what was
identified pre-anonymization. We evaluated our approach using publicly
available datasets from the financial industry. Our evaluation confirmed that
the approach was able to produce synthetic datasets that preserved a high level
of subgroup differentiation as identified initially in the original dataset.
Such a distinction was maintained while having distinctly different records
between the synthetic and original dataset. Finally, we packed the above end to
end process into what we call Utility Guaranteed Deep Privacy (UGDP) system.
UGDP can be easily extended to onboard alternative generative approaches such
as GANs to synthesize tabular data.

negative:
Generative Adversarial Networks (Goodfellow et al., 2014), a major
breakthrough in the field of generative modeling, learn a discriminator to
estimate some distance between the target and the candidate distributions.
  This paper examines mathematical issues regarding the way the gradients for
the generative model are computed in this context, and notably how to take into
account how the discriminator itself depends on the generator parameters.
  A unifying methodology is presented to define mathematically sound training
objectives for generative models taking this dependency into account in a
robust way, covering both GAN, VAE and some GAN variants as particular cases.

negative:
In modern data analysis, random sampling is an efficient and widely-used
strategy to overcome the computational difficulties brought by large sample
size. In previous studies, researchers conducted random sampling which is
according to the input data but independent on the response variable, however
the response variable may also be informative for sampling. In this paper we
propose an adaptive sampling called the gradient-based sampling which is
dependent on both the input data and the output for fast solving of
least-square (LS) problems. We draw the data points by random sampling from the
full data according to their gradient values. This sampling is computationally
saving, since the running time of computing the sampling probabilities is
reduced to O(nd) where n is the full sample size and d is the dimension of the
input. Theoretically, we establish an error bound analysis of the general
importance sampling with respect to LS solution from full data. The result
establishes an improved performance of the use of our gradient- based sampling.
Synthetic and real data sets are used to empirically argue that the
gradient-based sampling has an obvious advantage over existing sampling methods
from two aspects of statistical efficiency and computational saving.

negative:
Generative Adversarial Networks (GANs) have extended deep learning to complex
generation and translation tasks across different data modalities. However,
GANs are notoriously difficult to train: Mode collapse and other instabilities
in the training process often degrade the quality of the generated results,
such as images. This paper presents a new technique called TaylorGAN for
improving GANs by discovering customized loss functions for each of its two
networks. The loss functions are parameterized as Taylor expansions and
optimized through multiobjective evolution. On an image-to-image translation
benchmark task, this approach qualitatively improves generated image quality
and quantitatively improves two independent GAN performance metrics. It
therefore forms a promising approach for applying GANs to more challenging
tasks in the future.

negative:
In many applications of computer graphics, art and design, it is desirable
for a user to provide intuitive non-image input, such as text, sketch, stroke,
graph or layout, and have a computer system automatically generate
photo-realistic images that adhere to the input content. While classic works
that allow such automatic image content generation have followed a framework of
image retrieval and composition, recent advances in deep generative models such
as generative adversarial networks (GANs), variational autoencoders (VAEs), and
flow-based methods have enabled more powerful and versatile image generation
tasks. This paper reviews recent works for image synthesis given intuitive user
input, covering advances in input versatility, image generation methodology,
benchmark datasets, and evaluation metrics. This motivates new perspectives on
input representation and interactivity, cross pollination between major image
generation paradigms, and evaluation and comparison of generation methods.

negative:
Generative concept representations have three major advantages over
discriminative ones: they can represent uncertainty, they support integration
of learning and reasoning, and they are good for unsupervised and
semi-supervised learning. We discuss probabilistic and generative deep
learning, which generative concept representations are based on, and the use of
variational autoencoders and generative adversarial networks for learning
generative concept representations, particularly for concepts whose data are
sequences, structured data or graphs.

negative:
Generative adversarial networks or GANs are a type of generative modeling
framework. GANs involve a pair of neural networks engaged in a competition in
iteratively creating fake data, indistinguishable from the real data. One
notable application of GANs is developing fake human faces, also known as "deep
fakes," due to the deep learning algorithms at the core of the GAN framework.
Measuring the quality of the generated images is inherently subjective but
attempts to objectify quality using standardized metrics have been made. One
example of objective metrics is the Frechet Inception Distance (FID), which
measures the difference between distributions of feature vectors for two
separate datasets of images. There are situations that images with low
perceptual qualities are not assigned appropriate FID scores. We propose to
improve the robustness of the evaluation process by integrating lower-level
features to cover a wider array of visual defects. Our proposed method
integrates three levels of feature abstractions to evaluate the quality of
generated images. Experimental evaluations show better performance of the
proposed method for distorted images.

negative:
Generative Adversarial Networks (GAN) have received wide attention in the
machine learning field for their potential to learn high-dimensional, complex
real data distribution. Specifically, they do not rely on any assumptions about
the distribution and can generate real-like samples from latent space in a
simple manner. This powerful property leads GAN to be applied to various
applications such as image synthesis, image attribute editing, image
translation, domain adaptation and other academic fields. In this paper, we aim
to discuss the details of GAN for those readers who are familiar with, but do
not comprehend GAN deeply or who wish to view GAN from various perspectives. In
addition, we explain how GAN operates and the fundamental meaning of various
objective functions that have been suggested recently. We then focus on how the
GAN can be combined with an autoencoder framework. Finally, we enumerate the
GAN variants that are applied to various tasks and other fields for those who
are interested in exploiting GAN for their research.

negative:
Assessing generative models is not an easy task. Generative models should
synthesize graphs which are not replicates of real networks but show
topological features similar to real graphs. We introduce an approach for
assessing graph generative models using graph classifiers. The inability of an
established graph classifier for distinguishing real and synthesized graphs
could be considered as a performance measurement for graph generators.

negative:
This is a tutorial and survey paper on Generative Adversarial Network (GAN),
adversarial autoencoders, and their variants. We start with explaining
adversarial learning and the vanilla GAN. Then, we explain the conditional GAN
and DCGAN. The mode collapse problem is introduced and various methods,
including minibatch GAN, unrolled GAN, BourGAN, mixture GAN, D2GAN, and
Wasserstein GAN, are introduced for resolving this problem. Then, maximum
likelihood estimation in GAN are explained along with f-GAN, adversarial
variational Bayes, and Bayesian GAN. Then, we cover feature matching in GAN,
InfoGAN, GRAN, LSGAN, energy-based GAN, CatGAN, MMD GAN, LapGAN, progressive
GAN, triple GAN, LAG, GMAN, AdaGAN, CoGAN, inverse GAN, BiGAN, ALI, SAGAN,
Few-shot GAN, SinGAN, and interpolation and evaluation of GAN. Then, we
introduce some applications of GAN such as image-to-image translation
(including PatchGAN, CycleGAN, DeepFaceDrawing, simulated GAN, interactive
GAN), text-to-image translation (including StackGAN), and mixing image
characteristics (including FineGAN and MixNMatch). Finally, we explain the
autoencoders based on adversarial learning including adversarial autoencoder,
PixelGAN, and implicit autoencoder.

negative:
This paper examines to what degree current deep learning architectures for
image caption generation capture spatial language. On the basis of the
evaluation of examples of generated captions from the literature we argue that
systems capture what objects are in the image data but not where these objects
are located: the captions generated by these systems are the output of a
language model conditioned on the output of an object detector that cannot
capture fine-grained location information. Although language models provide
useful knowledge for image captions, we argue that deep learning image
captioning architectures should also model geometric relations between objects.

negative:
Generative Adversarial Networks (GANs) have been used extensively and quite
successfully for unsupervised learning. As GANs don't approximate an explicit
probability distribution, it's an interesting study to inspect the latent space
representations learned by GANs. The current work seeks to push the boundaries
of such inspection methods to further understand in more detail the manifold
being learned by GANs. Various interpolation and extrapolation techniques along
with vector arithmetic is used to understand the learned manifold. We show
through experiments that GANs indeed learn a data probability distribution
rather than memorize images/data. Further, we prove that GANs encode
semantically relevant information in the learned probability distribution. The
experiments have been performed on two publicly available datasets - Large
Scale Scene Understanding (LSUN) and CelebA.

negative:
In this paper, a novel strategy of Secure Steganograpy based on Generative
Adversarial Networks is proposed to generate suitable and secure covers for
steganography. The proposed architecture has one generative network, and two
discriminative networks. The generative network mainly evaluates the visual
quality of the generated images for steganography, and the discriminative
networks are utilized to assess their suitableness for information hiding.
Different from the existing work which adopts Deep Convolutional Generative
Adversarial Networks, we utilize another form of generative adversarial
networks. By using this new form of generative adversarial networks,
significant improvements are made on the convergence speed, the training
stability and the image quality. Furthermore, a sophisticated steganalysis
network is reconstructed for the discriminative network, and the network can
better evaluate the performance of the generated images. Numerous experiments
are conducted on the publicly available datasets to demonstrate the
effectiveness and robustness of the proposed method.

negative:
Devising indicative evaluation metrics for the image generation task remains
an open problem. The most widely used metric for measuring the similarity
between real and generated images has been the Fr\'echet Inception Distance
(FID) score. Because it does not differentiate the fidelity and diversity
aspects of the generated images, recent papers have introduced variants of
precision and recall metrics to diagnose those properties separately. In this
paper, we show that even the latest version of the precision and recall metrics
are not reliable yet. For example, they fail to detect the match between two
identical distributions, they are not robust against outliers, and the
evaluation hyperparameters are selected arbitrarily. We propose density and
coverage metrics that solve the above issues. We analytically and
experimentally show that density and coverage provide more interpretable and
reliable signals for practitioners than the existing metrics. Code:
https://github.com/clovaai/generative-evaluation-prdc.

negative:
Generative adversarial networks (GANs) are increasingly attracting attention
in the computer vision, natural language processing, speech synthesis and
similar domains. However, evaluating the performance of GANs is still an open
and challenging problem. Existing evaluation metrics primarily measure the
dissimilarity between real and generated images using automated statistical
methods. They often require large sample sizes for evaluation and do not
directly reflect human perception of image quality. In this work, we introduce
an evaluation metric called Neuroscore, for evaluating the performance of GANs,
that more directly reflects psychoperceptual image quality through the
utilization of brain signals. Our results show that Neuroscore has superior
performance to the current evaluation metrics in that: (1) It is more
consistent with human judgment; (2) The evaluation process needs much smaller
numbers of samples; and (3) It is able to rank the quality of images on a per
GAN basis. A convolutional neural network (CNN) based neuro-AI interface is
proposed to predict Neuroscore from GAN-generated images directly without the
need for neural responses. Importantly, we show that including neural responses
during the training phase of the network can significantly improve the
prediction capability of the proposed model. Codes and data can be referred at
this link: https://github.com/villawang/Neuro-AI-Interface.

negative:
Generative Adversarial Networks (GANs) have become a dominant class of
generative models. In recent years, GAN variants have yielded especially
impressive results in the synthesis of a variety of forms of data. Examples
include compelling natural and artistic images, textures, musical sequences,
and 3D object files. However, one obvious synthesis candidate is missing. In
this work, we answer one of deep learning's most pressing questions: GAN you do
the GAN GAN? That is, is it possible to train a GAN to model a distribution of
GANs? We release the full source code for this project under the MIT license.

negative:
While Generative Adversarial Networks (GANs) show increasing performance and
the level of realism is becoming indistinguishable from natural images, this
also comes with high demands on data and computation. We show that
state-of-the-art GAN models -- such as they are being publicly released by
researchers and industry -- can be used for a range of applications beyond
unconditional image generation. We achieve this by an iterative scheme that
also allows gaining control over the image generation process despite the
highly non-linear latent spaces of the latest GAN models. We demonstrate that
this opens up the possibility to re-use state-of-the-art, difficult to train,
pre-trained GANs with a high level of control even if only black-box access is
granted. Our work also raises concerns and awareness that the use cases of a
published GAN model may well reach beyond the creators' intention, which needs
to be taken into account before a full public release. Code is available at
https://github.com/a514514772/hijackgan.

negative:
Despite recent advances, the remaining bottlenecks in deep generative models
are necessity of extensive training and difficulties with generalization from
small number of training examples. We develop a new generative model called
Generative Matching Network which is inspired by the recently proposed matching
networks for one-shot learning in discriminative tasks. By conditioning on the
additional input dataset, our model can instantly learn new concepts that were
not available in the training data but conform to a similar generative process.
The proposed framework does not explicitly restrict diversity of the
conditioning data and also does not require an extensive inference procedure
for training or adaptation. Our experiments on the Omniglot dataset demonstrate
that Generative Matching Networks significantly improve predictive performance
on the fly as more additional data is available and outperform existing state
of the art conditional generative models.

negative:
Automated evaluation metrics as a stand-in for manual evaluation are an
essential part of the development of text-generation tasks such as text
summarization. However, while the field has progressed, our standard metrics
have not -- for nearly 20 years ROUGE has been the standard evaluation in most
summarization papers. In this paper, we make an attempt to re-evaluate the
evaluation method for text summarization: assessing the reliability of
automatic metrics using top-scoring system outputs, both abstractive and
extractive, on recently popular datasets for both system-level and
summary-level evaluation settings. We find that conclusions about evaluation
metrics on older datasets do not necessarily hold on modern datasets and
systems.

negative:
Tremendous advances in image restoration tasks such as denoising and
super-resolution have been achieved using neural networks. Such approaches
generally employ very deep architectures, large number of parameters, large
receptive fields and high nonlinear modeling capacity. In order to obtain
efficient and fast image restoration networks one should improve upon the above
mentioned requirements.
  In this paper we propose a novel activation function, the multi-bin trainable
linear unit (MTLU), for increasing the nonlinear modeling capacity together
with lighter and shallower networks. We validate the proposed fast image
restoration networks for image denoising (FDnet) and super-resolution (FSRnet)
on standard benchmarks. We achieve large improvements in both memory and
runtime over current state-of-the-art for comparable or better PSNR accuracies.

negative:
As information exists in various modalities in real world, effective
interaction and fusion among multimodal information plays a key role for the
creation and perception of multimodal data in computer vision and deep learning
research. With superb power in modelling the interaction among multimodal
information, multimodal image synthesis and editing has become a hot research
topic in recent years. Instead of providing explicit guidance for network
training, multimodal guidance offers intuitive and flexible means for image
synthesis and editing. On the other hand, this field is also facing several
challenges in alignment of features with inherent modality gaps, synthesis of
high-resolution images, faithful evaluation metrics, etc. In this survey, we
comprehensively contextualize the advance of the recent multimodal image
synthesis and editing and formulate taxonomies according to data modality and
model architectures. We start with an introduction to different types of
guidance modalities in image synthesis and editing. We then describe multimodal
image synthesis and editing approaches extensively with detailed frameworks
including Generative Adversarial Networks (GANs), Auto-regressive models,
Diffusion models, Neural Radiance Fields (NeRF) and other methods. This is
followed by a comprehensive description of benchmark datasets and corresponding
evaluation metrics as widely adopted in multimodal image synthesis and editing,
as well as detailed comparisons of various synthesis methods with analysis of
respective advantages and limitations. Finally, we provide insights about the
current research challenges and possible directions for future research. We
hope this survey could lay a sound and valuable foundation for future
development of multimodal image synthesis and editing. A project associated
with this survey is available at https://github.com/fnzhan/MISE.

negative:
Generative adversarial networks (GANs) are a novel approach to generative
modelling, a task whose goal it is to learn a distribution of real data points.
They have often proved difficult to train: GANs are unlike many techniques in
machine learning, in that they are best described as a two-player game between
a discriminator and generator. This has yielded both unreliability in the
training process, and a general lack of understanding as to how GANs converge,
and if so, to what. The purpose of this dissertation is to provide an account
of the theory of GANs suitable for the mathematician, highlighting both
positive and negative results. This involves identifying the problems when
training GANs, and how topological and game-theoretic perspectives of GANs have
contributed to our understanding and improved our techniques in recent years.

negative:
With the recent progress in Generative Adversarial Networks (GANs), it is
imperative for media and visual forensics to develop detectors which can
identify and attribute images to the model generating them. Existing works have
shown to attribute images to their corresponding GAN sources with high
accuracy. However, these works are limited to a closed set scenario, failing to
generalize to GANs unseen during train time and are therefore, not scalable
with a steady influx of new GANs. We present an iterative algorithm for
discovering images generated from previously unseen GANs by exploiting the fact
that all GANs leave distinct fingerprints on their generated images. Our
algorithm consists of multiple components including network training,
out-of-distribution detection, clustering, merge and refine steps. Through
extensive experiments, we show that our algorithm discovers unseen GANs with
high accuracy and also generalizes to GANs trained on unseen real datasets. We
additionally apply our algorithm to attribution and discovery of GANs in an
online fashion as well as to the more standard task of real/fake detection. Our
experiments demonstrate the effectiveness of our approach to discover new GANs
and can be used in an open-world setup.

negative:
Generative adversarial networks (GANs) are a hot research topic recently.
GANs have been widely studied since 2014, and a large number of algorithms have
been proposed. However, there is few comprehensive study explaining the
connections among different GANs variants, and how they have evolved. In this
paper, we attempt to provide a review on various GANs methods from the
perspectives of algorithms, theory, and applications. Firstly, the motivations,
mathematical representations, and structure of most GANs algorithms are
introduced in details. Furthermore, GANs have been combined with other machine
learning algorithms for specific applications, such as semi-supervised
learning, transfer learning, and reinforcement learning. This paper compares
the commonalities and differences of these GANs methods. Secondly, theoretical
issues related to GANs are investigated. Thirdly, typical applications of GANs
in image processing and computer vision, natural language processing, music,
speech and audio, medical field, and data science are illustrated. Finally, the
future open research problems for GANs are pointed out.

negative:
We demonstrate a conditional autoregressive pipeline for efficient music
recomposition, based on methods presented in van den Oord et al.(2017).
Recomposition (Casal & Casey, 2010) focuses on reworking existing musical
pieces, adhering to structure at a high level while also re-imagining other
aspects of the work. This can involve reuse of pre-existing themes or parts of
the original piece, while also requiring the flexibility to generate new
content at different levels of granularity. Applying the aforementioned
modeling pipeline to recomposition, we show diverse and structured generation
conditioned on chord sequence annotations.

negative:
Deep learning has achieved overwhelming success, spanning from discriminative
models to generative models. In particular, deep generative models have
facilitated a new level of performance in a myriad of areas, ranging from media
manipulation to sanitized dataset generation. Despite the great success, the
potential risks of privacy breach caused by generative models have not been
analyzed systematically. In this paper, we focus on membership inference attack
against deep generative models that reveals information about the training data
used for victim models. Specifically, we present the first taxonomy of
membership inference attacks, encompassing not only existing attacks but also
our novel ones. In addition, we propose the first generic attack model that can
be instantiated in a large range of settings and is applicable to various kinds
of deep generative models. Moreover, we provide a theoretically grounded attack
calibration technique, which consistently boosts the attack performance in all
cases, across different attack settings, data modalities, and training
configurations. We complement the systematic analysis of attack performance by
a comprehensive experimental study, that investigates the effectiveness of
various attacks w.r.t. model type and training configurations, over three
diverse application scenarios (i.e., images, medical data, and location data).

negative:
Generative models often use human evaluations to measure the perceived
quality of their outputs. Automated metrics are noisy indirect proxies, because
they rely on heuristics or pretrained embeddings. However, up until now, direct
human evaluation strategies have been ad-hoc, neither standardized nor
validated. Our work establishes a gold standard human benchmark for generative
realism. We construct Human eYe Perceptual Evaluation (HYPE) a human benchmark
that is (1) grounded in psychophysics research in perception, (2) reliable
across different sets of randomly sampled outputs from a model, (3) able to
produce separable model performances, and (4) efficient in cost and time. We
introduce two variants: one that measures visual perception under adaptive time
constraints to determine the threshold at which a model's outputs appear real
(e.g. 250ms), and the other a less expensive variant that measures human error
rate on fake and real images sans time constraints. We test HYPE across six
state-of-the-art generative adversarial networks and two sampling techniques on
conditional and unconditional image generation using four datasets: CelebA,
FFHQ, CIFAR-10, and ImageNet. We find that HYPE can track model improvements
across training epochs, and we confirm via bootstrap sampling that HYPE
rankings are consistent and replicable.

negative:
Identifying the named entities mentioned in text would enrich many semantic
applications at the downstream level. However, due to the predominant usage of
colloquial language in microblogs, the named entity recognition (NER) in
Chinese microblogs experience significant performance deterioration, compared
with performing NER in formal Chinese corpus. In this paper, we propose a
simple yet effective neural framework to derive the character-level embeddings
for NER in Chinese text, named ME-CNER. A character embedding is derived with
rich semantic information harnessed at multiple granularities, ranging from
radical, character to word levels. The experimental results demonstrate that
the proposed approach achieves a large performance improvement on Weibo dataset
and comparable performance on MSRA news dataset with lower computational cost
against the existing state-of-the-art alternatives.