Stabilizing Adversarial Nets with Prediction Methods
Adversarial neural networks solve many important problems in data science, but are notoriously difficult to train. These difficulties come from the fact that optimal weights for adversarial nets correspond to saddle points, and not minimizers, of the loss function. The alternating stochastic gradient methods typically used for such problems do not reliably converge to saddle points, and when convergence does happen it is often highly sensitive to learning rates. We propose a simple modification of stochastic gradient descent that stabilizes adversarial networks. We show, both in theory and practice, that the proposed method reliably converges to saddle points. This makes adversarial networks less likely to "collapse," and enables faster training with larger learning rates.
 This paper proposes a simple modification to the standard alternating stochastic gradient method for GAN training, which stabilizes training, by adding a prediction step. This is a clever and useful idea, and the paper is very well written. The proposed method is very clearly motivated, both intuitively and mathematically, and the authors also provide theoretical guarantees on its convergence behavior. I particularly liked the analogy with the damped harmonic oscillator. The experiments are well designed and provide clear evidence in favor of the usefulness of the proposed technique. I believe that the method proposed in this paper will have a significant impact in the area of GAN training. I have only one minor question: in the prediction step, why not use a step size, say , such that the "amount of predition" may be adjusted?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Stabilizing Adversarial Nets with Prediction Methods
Adversarial neural networks solve many important problems in data science, but are notoriously difficult to train. These difficulties come from the fact that optimal weights for adversarial nets correspond to saddle points, and not minimizers, of the loss function. The alternating stochastic gradient methods typically used for such problems do not reliably converge to saddle points, and when convergence does happen it is often highly sensitive to learning rates. We propose a simple modification of stochastic gradient descent that stabilizes adversarial networks. We show, both in theory and practice, that the proposed method reliably converges to saddle points. This makes adversarial networks less likely to "collapse," and enables faster training with larger learning rates.
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 a simple modification to the standard alternating stochastic gradient method for GAN training, which stabilizes training, by adding a prediction step. This is a clever and useful idea, and the paper is very well written. The proposed method is very clearly motivated, both intuitively and mathematically, and the authors also provide theoretical guarantees on its convergence behavior. I particularly liked the analogy with the damped harmonic oscillator. The experiments are well designed and provide clear evidence in favor of the usefulness of the proposed technique. I believe that the method proposed in this paper will have a significant impact in the area of GAN training. I have only one minor question: in the prediction step, why not use a step size, say , such that the "amount of predition" may be adjusted?
assistant
 This paper proposes a simple modification to the standard alternating stochastic gradient method for GAN training, which stabilizes training, by adding a prediction step. This is a clever and useful idea, and the paper is very well written. The proposed method is very clearly motivated, both intuitively and mathematically, and the authors also provide theoretical guarantees on its convergence behavior. I particularly liked the analogy with the damped harmonic oscillator. The experiments are well designed and provide clear evidence in favor of the usefulness of the proposed technique. I believe that the method proposed in this paper will have a significant impact in the area of GAN training. I have only one minor question: in the prediction step, why not use a step size, say , such that the "amount of predition" may be adjusted?