Don*t let your Discriminator be fooled
Generative Adversarial Networks are one of the leading tools in generative modeling, image editing and content creation. However, they are hard to train as they require a delicate balancing act between two deep networks fighting a never ending duel. Some of the most promising adversarial models today minimize a Wasserstein objective. It is smoother and more stable to optimize. In this paper, we show that the Wasserstein distance is just one out of a large family of objective functions that yield these properties. By making the discriminator of a GAN robust to adversarial attacks we can turn any GAN objective into a smooth and stable loss. We experimentally show that any GAN objective, including Wasserstein GANs, benefit from adversarial robustness both quantitatively and qualitatively. The training additionally becomes more robust to suboptimal choices of hyperparameters, model architectures, or objective functions.
 The paper proposed a systematic way of training GANs with robustness regularization terms. Using the proposed method, training GANs is smoother and pros - The paper is solving an important problem of training GANs in a robust manner. The idea of designing regularization terms is also explored in other domains of computer vision research, and it*s nice to see the its power in training GANs. - The paper provides detailed proofs and analysis of the approach, and visualizations of the regularization term help people to understand the ideas. - The presentation of the approach makes sense, and experimental results using several different GANs methods and competing regularization methods are extensive and good in general cons - I didn*t find major issues of the paper. I think code in the paper should be made public as it could potentially be very useful for training GANs in general.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Don*t let your Discriminator be fooled
Generative Adversarial Networks are one of the leading tools in generative modeling, image editing and content creation. However, they are hard to train as they require a delicate balancing act between two deep networks fighting a never ending duel. Some of the most promising adversarial models today minimize a Wasserstein objective. It is smoother and more stable to optimize. In this paper, we show that the Wasserstein distance is just one out of a large family of objective functions that yield these properties. By making the discriminator of a GAN robust to adversarial attacks we can turn any GAN objective into a smooth and stable loss. We experimentally show that any GAN objective, including Wasserstein GANs, benefit from adversarial robustness both quantitatively and qualitatively. The training additionally becomes more robust to suboptimal choices of hyperparameters, model architectures, or objective functions.
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 proposed a systematic way of training GANs with robustness regularization terms. Using the proposed method, training GANs is smoother and pros - The paper is solving an important problem of training GANs in a robust manner. The idea of designing regularization terms is also explored in other domains of computer vision research, and it*s nice to see the its power in training GANs. - The paper provides detailed proofs and analysis of the approach, and visualizations of the regularization term help people to understand the ideas. - The presentation of the approach makes sense, and experimental results using several different GANs methods and competing regularization methods are extensive and good in general cons - I didn*t find major issues of the paper. I think code in the paper should be made public as it could potentially be very useful for training GANs in general.
assistant
 The paper proposed a systematic way of training GANs with robustness regularization terms. Using the proposed method, training GANs is smoother and pros - The paper is solving an important problem of training GANs in a robust manner. The idea of designing regularization terms is also explored in other domains of computer vision research, and it*s nice to see the its power in training GANs. - The paper provides detailed proofs and analysis of the approach, and visualizations of the regularization term help people to understand the ideas. - The presentation of the approach makes sense, and experimental results using several different GANs methods and competing regularization methods are extensive and good in general cons - I didn*t find major issues of the paper. I think code in the paper should be made public as it could potentially be very useful for training GANs in general.