Mode Regularized Generative Adversarial Networks
Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution during the early phases of training, thus providing a unified solution to the missing modes problem.
This paper does a good job of clearly articulating a problem in contemporary training of GANs, coming up with an intuitive solution via regularizers in addition to optimizing only the discriminator score, and conducting clever experiments to show that the regularizers have the intended effect. There are recent related and improved GAN variants (ALI, VAEGAN, potentially others), which are included in qualitative comparisons, but not quantitative. It would be interesting to see whether these other types of modified GANs already make some progress in addressing the missing modes problem. If code is available for those methods, the paper could be strengthened a lot by running the mode-missing benchmarks on them (even if it turns out that a *competing* method can get a better result in some cases). The experiments on digits and faces are good for validating the proposed regularizers. However, if the authors can show better results on CIFAR-10, ImageNet, MS-COCO or some other more diverse and challenging dataset, I would be more convinced of the value of the proposed method.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Mode Regularized Generative Adversarial Networks
Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution during the early phases of training, thus providing a unified solution to the missing modes problem.
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 does a good job of clearly articulating a problem in contemporary training of GANs, coming up with an intuitive solution via regularizers in addition to optimizing only the discriminator score, and conducting clever experiments to show that the regularizers have the intended effect. There are recent related and improved GAN variants (ALI, VAEGAN, potentially others), which are included in qualitative comparisons, but not quantitative. It would be interesting to see whether these other types of modified GANs already make some progress in addressing the missing modes problem. If code is available for those methods, the paper could be strengthened a lot by running the mode-missing benchmarks on them (even if it turns out that a *competing* method can get a better result in some cases). The experiments on digits and faces are good for validating the proposed regularizers. However, if the authors can show better results on CIFAR-10, ImageNet, MS-COCO or some other more diverse and challenging dataset, I would be more convinced of the value of the proposed method.
assistant
This paper does a good job of clearly articulating a problem in contemporary training of GANs, coming up with an intuitive solution via regularizers in addition to optimizing only the discriminator score, and conducting clever experiments to show that the regularizers have the intended effect. There are recent related and improved GAN variants (ALI, VAEGAN, potentially others), which are included in qualitative comparisons, but not quantitative. It would be interesting to see whether these other types of modified GANs already make some progress in addressing the missing modes problem. If code is available for those methods, the paper could be strengthened a lot by running the mode-missing benchmarks on them (even if it turns out that a *competing* method can get a better result in some cases). The experiments on digits and faces are good for validating the proposed regularizers. However, if the authors can show better results on CIFAR-10, ImageNet, MS-COCO or some other more diverse and challenging dataset, I would be more convinced of the value of the proposed method.