Stochastic Conditional Generative Networks with Basis Decomposition
While generative adversarial networks (GANs) have revolutionized machine learning, a number of open questions remain to fully understand them and exploit their power. One of these questions is how to efficiently achieve proper diversity and sampling of the multi-mode data space. To address this, we introduce BasisGAN, a stochastic conditional multi-mode image generator. By exploiting the observation that a convolutional filter can be well approximated as a linear combination of a small set of basis elements, we learn a plug-and-played basis generator to stochastically generate basis elements, with just a few hundred of parameters, to fully embed stochasticity into convolutional filters. By sampling basis elements instead of filters, we dramatically reduce the cost of modeling the parameter space with no sacrifice on either image diversity or fidelity. To illustrate this proposed plug-and-play framework, we construct variants of BasisGAN based on state-of-the-art conditional image generation networks, and train the networks by simply plugging in a basis generator, without additional auxiliary components, hyperparameters, or training objectives. The experimental success is complemented with theoretical results indicating how the perturbations introduced by the proposed sampling of basis elements can propagate to the appearance of generated images.
In this paper, the authors introduce BasisGAN, a novel method for introducing stochasticity in conditional GANs, i.e., a way of conducting one-to-many mappings. This is a good addition in the literature as: (a) most of the widely-used conditional GANs such as pix2pix (Isola et al., 2016) or pix2pixHD (Wang et al., 2018) are deterministic (i.e., for a specific input a single output is always generated), (b) it improves upon the current SOTA in one-to-many mappings, (c) it is very useful application-wise. As also stated in the paper, there is a number of applications where this method is handy (e.g., converting a sketch to images varying in colors, etc.). I am leaning towards accepting this paper as this work is well-motivated and found the idea of using the basis generator to learn the bases for the generation of the parameters quite interesting. This is the main contribution and difference of this paper in comparison to DCFNet (Qiu et al., ICML 2018), where the bases are not learned. Nevertheless, I have the following questions/requests: - How can we tell that the generated bases are indeed bases (e.g., are they orthogonal?) - Please report the number of parameters used in your implementation in comparison to the rest of the methods. - Please provide qualitative results against the compared methods and especially against DSGAN (Qin et al., 2018).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Stochastic Conditional Generative Networks with Basis Decomposition
While generative adversarial networks (GANs) have revolutionized machine learning, a number of open questions remain to fully understand them and exploit their power. One of these questions is how to efficiently achieve proper diversity and sampling of the multi-mode data space. To address this, we introduce BasisGAN, a stochastic conditional multi-mode image generator. By exploiting the observation that a convolutional filter can be well approximated as a linear combination of a small set of basis elements, we learn a plug-and-played basis generator to stochastically generate basis elements, with just a few hundred of parameters, to fully embed stochasticity into convolutional filters. By sampling basis elements instead of filters, we dramatically reduce the cost of modeling the parameter space with no sacrifice on either image diversity or fidelity. To illustrate this proposed plug-and-play framework, we construct variants of BasisGAN based on state-of-the-art conditional image generation networks, and train the networks by simply plugging in a basis generator, without additional auxiliary components, hyperparameters, or training objectives. The experimental success is complemented with theoretical results indicating how the perturbations introduced by the proposed sampling of basis elements can propagate to the appearance of generated images.
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>



In this paper, the authors introduce BasisGAN, a novel method for introducing stochasticity in conditional GANs, i.e., a way of conducting one-to-many mappings. This is a good addition in the literature as: (a) most of the widely-used conditional GANs such as pix2pix (Isola et al., 2016) or pix2pixHD (Wang et al., 2018) are deterministic (i.e., for a specific input a single output is always generated), (b) it improves upon the current SOTA in one-to-many mappings, (c) it is very useful application-wise. As also stated in the paper, there is a number of applications where this method is handy (e.g., converting a sketch to images varying in colors, etc.). I am leaning towards accepting this paper as this work is well-motivated and found the idea of using the basis generator to learn the bases for the generation of the parameters quite interesting. This is the main contribution and difference of this paper in comparison to DCFNet (Qiu et al., ICML 2018), where the bases are not learned. Nevertheless, I have the following questions/requests: - How can we tell that the generated bases are indeed bases (e.g., are they orthogonal?) - Please report the number of parameters used in your implementation in comparison to the rest of the methods. - Please provide qualitative results against the compared methods and especially against DSGAN (Qin et al., 2018).
assistant
In this paper, the authors introduce BasisGAN, a novel method for introducing stochasticity in conditional GANs, i.e., a way of conducting one-to-many mappings. This is a good addition in the literature as: (a) most of the widely-used conditional GANs such as pix2pix (Isola et al., 2016) or pix2pixHD (Wang et al., 2018) are deterministic (i.e., for a specific input a single output is always generated), (b) it improves upon the current SOTA in one-to-many mappings, (c) it is very useful application-wise. As also stated in the paper, there is a number of applications where this method is handy (e.g., converting a sketch to images varying in colors, etc.). I am leaning towards accepting this paper as this work is well-motivated and found the idea of using the basis generator to learn the bases for the generation of the parameters quite interesting. This is the main contribution and difference of this paper in comparison to DCFNet (Qiu et al., ICML 2018), where the bases are not learned. Nevertheless, I have the following questions/requests: - How can we tell that the generated bases are indeed bases (e.g., are they orthogonal?) - Please report the number of parameters used in your implementation in comparison to the rest of the methods. - Please provide qualitative results against the compared methods and especially against DSGAN (Qin et al., 2018).