Generative Adversarial Parallelization
Generative Adversarial Networks (GAN) have become one of the most studied frameworks for unsupervised learning due to their intuitive formulation. They have also been shown to be capable of generating convincing examples in limited domains, such as low-resolution images. However, they still prove difficult to train in practice and tend to ignore modes of the data generating distribution. Quantitatively capturing effects such as mode coverage and more generally the quality of the generative model still remain elusive. We propose Generative Adversarial Parallelization (GAP), a framework in which many GANs or their variants are trained simultaneously, exchanging their discriminators. This eliminates the tight coupling between a generator and discriminator, leading to improved convergence and improved coverage of modes. We also propose an improved variant of the recently proposed Generative Adversarial Metric and show how it can score individual GANs or their collections under the GAP model.
This paper proposes an extension of the GAN framework known as GAP whereby multiple generators and discriminators are trained in parallel. The generator/discriminator pairing is shuffled according to a periodic schedule. Pros: + The proposed approach is simple and easy to replicate. Cons: - The paper is confusing to read. - The results are suggestive but do not conclusively show a performance win for GAP. The main argument of the paper is that GAP leads to improved convergence and improved coverage of modes. The coverage visualizations are suggestive but there still is not enough evidence to conclude that GAP is in fact improving coverage. And for convergence it is difficult to assess the effect of GAP on the basis of learning curves. The proposed GAM-II metric is circular in that model performance depends on the collection of baselines the model is being compared with. Estimating likelihood via AIS seems to be a promising way to evaluate, as does using the Inception score. Perhaps a more systematic way to determine GAP*s effect would be to set up a grid search of hyperparameters and train an equal number of GANs and GAP-GANs for each setting. Then a histogram over final Inception scores or likelihood estimates of the trained models would help to show whether GAP tended to produce better models. Overall the approach seems promising but there are too many open questions regarding the paper in its current form. * Section 2: *Remark that when...* => seems like a to-do. * Section A.1: The proposed metric is not described in adequate detail.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Generative Adversarial Parallelization
Generative Adversarial Networks (GAN) have become one of the most studied frameworks for unsupervised learning due to their intuitive formulation. They have also been shown to be capable of generating convincing examples in limited domains, such as low-resolution images. However, they still prove difficult to train in practice and tend to ignore modes of the data generating distribution. Quantitatively capturing effects such as mode coverage and more generally the quality of the generative model still remain elusive. We propose Generative Adversarial Parallelization (GAP), a framework in which many GANs or their variants are trained simultaneously, exchanging their discriminators. This eliminates the tight coupling between a generator and discriminator, leading to improved convergence and improved coverage of modes. We also propose an improved variant of the recently proposed Generative Adversarial Metric and show how it can score individual GANs or their collections under the GAP model.
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 an extension of the GAN framework known as GAP whereby multiple generators and discriminators are trained in parallel. The generator/discriminator pairing is shuffled according to a periodic schedule. Pros: + The proposed approach is simple and easy to replicate. Cons: - The paper is confusing to read. - The results are suggestive but do not conclusively show a performance win for GAP. The main argument of the paper is that GAP leads to improved convergence and improved coverage of modes. The coverage visualizations are suggestive but there still is not enough evidence to conclude that GAP is in fact improving coverage. And for convergence it is difficult to assess the effect of GAP on the basis of learning curves. The proposed GAM-II metric is circular in that model performance depends on the collection of baselines the model is being compared with. Estimating likelihood via AIS seems to be a promising way to evaluate, as does using the Inception score. Perhaps a more systematic way to determine GAP*s effect would be to set up a grid search of hyperparameters and train an equal number of GANs and GAP-GANs for each setting. Then a histogram over final Inception scores or likelihood estimates of the trained models would help to show whether GAP tended to produce better models. Overall the approach seems promising but there are too many open questions regarding the paper in its current form. * Section 2: *Remark that when...* => seems like a to-do. * Section A.1: The proposed metric is not described in adequate detail.
assistant
This paper proposes an extension of the GAN framework known as GAP whereby multiple generators and discriminators are trained in parallel. The generator/discriminator pairing is shuffled according to a periodic schedule. Pros: + The proposed approach is simple and easy to replicate. Cons: - The paper is confusing to read. - The results are suggestive but do not conclusively show a performance win for GAP. The main argument of the paper is that GAP leads to improved convergence and improved coverage of modes. The coverage visualizations are suggestive but there still is not enough evidence to conclude that GAP is in fact improving coverage. And for convergence it is difficult to assess the effect of GAP on the basis of learning curves. The proposed GAM-II metric is circular in that model performance depends on the collection of baselines the model is being compared with. Estimating likelihood via AIS seems to be a promising way to evaluate, as does using the Inception score. Perhaps a more systematic way to determine GAP*s effect would be to set up a grid search of hyperparameters and train an equal number of GANs and GAP-GANs for each setting. Then a histogram over final Inception scores or likelihood estimates of the trained models would help to show whether GAP tended to produce better models. Overall the approach seems promising but there are too many open questions regarding the paper in its current form. * Section 2: *Remark that when...* => seems like a to-do. * Section A.1: The proposed metric is not described in adequate detail.