Unrolled Generative Adversarial Networks
We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator*s objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator.
The paper introduces a technique for stabilizing the training of Generative Adversrial Networks by unrolling the inner (discriminator) optimization in the GAN loss function several steps and optimizing the generator with respect to the final state of this optimization process. The experimental evidence that this actually helps is very compelling: the 2d example shows a toy problem where this technique helps substantially, the LSTM MNIST generator example shows that the procedure helps with stabilizing the training of an unusual architecture of generator, and the image generation experiment, while not being definitive, is very convincing. For future work it would be interesting to see whether a method with smaller memory requirements could be devised based on similar principles. I strongly recommend to accept this paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Unrolled Generative Adversarial Networks
We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator*s objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator.
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 introduces a technique for stabilizing the training of Generative Adversrial Networks by unrolling the inner (discriminator) optimization in the GAN loss function several steps and optimizing the generator with respect to the final state of this optimization process. The experimental evidence that this actually helps is very compelling: the 2d example shows a toy problem where this technique helps substantially, the LSTM MNIST generator example shows that the procedure helps with stabilizing the training of an unusual architecture of generator, and the image generation experiment, while not being definitive, is very convincing. For future work it would be interesting to see whether a method with smaller memory requirements could be devised based on similar principles. I strongly recommend to accept this paper.
assistant
The paper introduces a technique for stabilizing the training of Generative Adversrial Networks by unrolling the inner (discriminator) optimization in the GAN loss function several steps and optimizing the generator with respect to the final state of this optimization process. The experimental evidence that this actually helps is very compelling: the 2d example shows a toy problem where this technique helps substantially, the LSTM MNIST generator example shows that the procedure helps with stabilizing the training of an unusual architecture of generator, and the image generation experiment, while not being definitive, is very convincing. For future work it would be interesting to see whether a method with smaller memory requirements could be devised based on similar principles. I strongly recommend to accept this paper.