Spectral Normalization for Generative Adversarial Networks
One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.
 This paper proposes "spectral normalization" -- constraining the spectral norm of the weights of each layer -- as a way to stabilize GAN training by in effect bounding the Lipschitz constant of the discriminator function. The paper derives efficient approximations for the spectral norm, as well as an analysis of its gradient. Experimental results on CIFAR-10 and STL-10 show improved Inception scores and FID scores using this method compared to other baselines and other weight normalization methods. Overall, this is a well-written paper that tackles an important open problem in training GANs using a well-motivated and relatively simple approach. The experimental results seem solid and seem to support the authors* claims. I agree with the anonymous reviewer that connections (and differences) to related work should be made clearer. Like the anonymous commenter, I also initially thought that the proposed "spectral normalization " is basically the same as "spectral norm regularization", but given the authors* feedback on this I think the differences should be made more explicit in the paper. Overall this seems to represent a strong step forward in improving the training of GANs, and I strongly recommend this paper for publication. Small Nits: Section 4: "In order to evaluate the efficacy of our experiment": I think you mean "approach". There are a few colloquial English usages which made me smile, e.g. * Sec 4.1.1. "As we prophesied ...", and in the paragraph below * "... is a tad slower ...".
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Spectral Normalization for Generative Adversarial Networks
One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.
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>



 This paper proposes "spectral normalization" -- constraining the spectral norm of the weights of each layer -- as a way to stabilize GAN training by in effect bounding the Lipschitz constant of the discriminator function. The paper derives efficient approximations for the spectral norm, as well as an analysis of its gradient. Experimental results on CIFAR-10 and STL-10 show improved Inception scores and FID scores using this method compared to other baselines and other weight normalization methods. Overall, this is a well-written paper that tackles an important open problem in training GANs using a well-motivated and relatively simple approach. The experimental results seem solid and seem to support the authors* claims. I agree with the anonymous reviewer that connections (and differences) to related work should be made clearer. Like the anonymous commenter, I also initially thought that the proposed "spectral normalization " is basically the same as "spectral norm regularization", but given the authors* feedback on this I think the differences should be made more explicit in the paper. Overall this seems to represent a strong step forward in improving the training of GANs, and I strongly recommend this paper for publication. Small Nits: Section 4: "In order to evaluate the efficacy of our experiment": I think you mean "approach". There are a few colloquial English usages which made me smile, e.g. * Sec 4.1.1. "As we prophesied ...", and in the paragraph below * "... is a tad slower ...".
assistant
 This paper proposes "spectral normalization" -- constraining the spectral norm of the weights of each layer -- as a way to stabilize GAN training by in effect bounding the Lipschitz constant of the discriminator function. The paper derives efficient approximations for the spectral norm, as well as an analysis of its gradient. Experimental results on CIFAR-10 and STL-10 show improved Inception scores and FID scores using this method compared to other baselines and other weight normalization methods. Overall, this is a well-written paper that tackles an important open problem in training GANs using a well-motivated and relatively simple approach. The experimental results seem solid and seem to support the authors* claims. I agree with the anonymous reviewer that connections (and differences) to related work should be made clearer. Like the anonymous commenter, I also initially thought that the proposed "spectral normalization " is basically the same as "spectral norm regularization", but given the authors* feedback on this I think the differences should be made more explicit in the paper. Overall this seems to represent a strong step forward in improving the training of GANs, and I strongly recommend this paper for publication. Small Nits: Section 4: "In order to evaluate the efficacy of our experiment": I think you mean "approach". There are a few colloquial English usages which made me smile, e.g. * Sec 4.1.1. "As we prophesied ...", and in the paragraph below * "... is a tad slower ...".