The GAN Landscape: Losses, Architectures, Regularization, and Normalization
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant amount of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of ``tricks". The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, and neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We reproduce the current state of the art and go beyond fairly exploring the GAN landscape. We discuss common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.
 The paper studies several different techniques for training GANs: the architecture chosen, the loss function of the discriminator and generator, and training techniques: normalization methods, ratio between updates of discriminator and generator, and regularization. The method is performing an empirical training study on three image datasets, modifying the training procedure (e.g. changing one of the parameters) and using different metrics to evaluate the performance of the trained network. Since the space of possible hyper-parameters , training algorithms, loss functions and network architecture is huge , the authors set a default training procedure, and in each numerical experiment freeze all techniques and parameters except for one or two which they modify and evaluate. The results of the paper do not give major insights into what are the preferred techniques for training GANs, and certainly not why and under what circumstances they*ll work. The authors recommend using non-saturated GANs loss and spectral normalization when training on new datasets, because these techniques achieved good performance metrics in most experiments. But there is no attempt to generalize the findings (e.g. new datasets not from original study, changing other parameters and then evaluating again if these techniques help etc.), not clear if the improvement in performance is statistically significant, how robust it is to changes in other parameters etc. The authors also rely mostly on the FID metric, but do not show if and how there is improvement upon visual inspection of the generated images (i.e. is resolution improved, is fraction of images that look clearly *unnatural* reduced etc.) The writing is understandable for the most part, but the paper seems to lack focus - there is no clear take home message. The authors use numerous jargon words to describe the techniques studied (e.g. dragon penalty, gradient penalty, spectral normalization, Gaussian process regression in the bandit setting) but they do not explain them, give mathematical formulations, or insights into their advantages/disadvantages, making it hard to the non-expert reader to understand what are these techniques and why are they introduced. With lack of clear novel insights, or at least more systematic study on additional datasets of the *winning* techniques and a sensitivity analysis, the paper does not give a valuable enough contribution to the field to merit publication.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
The GAN Landscape: Losses, Architectures, Regularization, and Normalization
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant amount of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of ``tricks". The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, and neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We reproduce the current state of the art and go beyond fairly exploring the GAN landscape. We discuss common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The paper studies several different techniques for training GANs: the architecture chosen, the loss function of the discriminator and generator, and training techniques: normalization methods, ratio between updates of discriminator and generator, and regularization. The method is performing an empirical training study on three image datasets, modifying the training procedure (e.g. changing one of the parameters) and using different metrics to evaluate the performance of the trained network. Since the space of possible hyper-parameters , training algorithms, loss functions and network architecture is huge , the authors set a default training procedure, and in each numerical experiment freeze all techniques and parameters except for one or two which they modify and evaluate. The results of the paper do not give major insights into what are the preferred techniques for training GANs, and certainly not why and under what circumstances they*ll work. The authors recommend using non-saturated GANs loss and spectral normalization when training on new datasets, because these techniques achieved good performance metrics in most experiments. But there is no attempt to generalize the findings (e.g. new datasets not from original study, changing other parameters and then evaluating again if these techniques help etc.), not clear if the improvement in performance is statistically significant, how robust it is to changes in other parameters etc. The authors also rely mostly on the FID metric, but do not show if and how there is improvement upon visual inspection of the generated images (i.e. is resolution improved, is fraction of images that look clearly *unnatural* reduced etc.) The writing is understandable for the most part, but the paper seems to lack focus - there is no clear take home message. The authors use numerous jargon words to describe the techniques studied (e.g. dragon penalty, gradient penalty, spectral normalization, Gaussian process regression in the bandit setting) but they do not explain them, give mathematical formulations, or insights into their advantages/disadvantages, making it hard to the non-expert reader to understand what are these techniques and why are they introduced. With lack of clear novel insights, or at least more systematic study on additional datasets of the *winning* techniques and a sensitivity analysis, the paper does not give a valuable enough contribution to the field to merit publication.
assistant
 The paper studies several different techniques for training GANs: the architecture chosen, the loss function of the discriminator and generator, and training techniques: normalization methods, ratio between updates of discriminator and generator, and regularization. The method is performing an empirical training study on three image datasets, modifying the training procedure (e.g. changing one of the parameters) and using different metrics to evaluate the performance of the trained network. Since the space of possible hyper-parameters , training algorithms, loss functions and network architecture is huge , the authors set a default training procedure, and in each numerical experiment freeze all techniques and parameters except for one or two which they modify and evaluate. The results of the paper do not give major insights into what are the preferred techniques for training GANs, and certainly not why and under what circumstances they*ll work. The authors recommend using non-saturated GANs loss and spectral normalization when training on new datasets, because these techniques achieved good performance metrics in most experiments. But there is no attempt to generalize the findings (e.g. new datasets not from original study, changing other parameters and then evaluating again if these techniques help etc. ), not clear if the improvement in performance is statistically significant, how robust it is to changes in other parameters etc. The authors also rely mostly on the FID metric, but do not show if and how there is improvement upon visual inspection of the generated images (i.e. is resolution improved, is fraction of images that look clearly *unnatural* reduced etc.) The writing is understandable for the most part, but the paper seems to lack focus - there is no clear take home message. The authors use numerous jargon words to describe the techniques studied (e.g. dragon penalty, gradient penalty, spectral normalization, Gaussian process regression in the bandit setting) but they do not explain them, give mathematical formulations, or insights into their advantages/disadvantages, making it hard to the non-expert reader to understand what are these techniques and why are they introduced. With lack of clear novel insights, or at least more systematic study on additional datasets of the *winning* techniques and a sensitivity analysis, the paper does not give a valuable enough contribution to the field to merit publication.