Incremental training of multi-generative adversarial networks
Generative neural networks map a standard, possibly distribution to a complex high-dimensional distribution, which represents the real world data set. However, a determinate input distribution as well as a specific architecture of neural networks may impose limitations on capturing the diversity in the high dimensional target space. To resolve this difficulty, we propose a training framework that greedily produce a series of generative adversarial networks that incrementally capture the diversity of the target space. We show theoretically and empirically that our training algorithm converges to the theoretically optimal distribution, the projection of the real distribution onto the convex hull of the network*s distribution space.
 This paper proposes a method for ensembling GAN’s for capturing diversity in the target space. This done by a convex combination of GAN’s that are sequentially trained by trying to approximate the real distribution by fixing the previous generators. The paper theoretically shows that this approach converges to the optimal theoretical distribution. Comments 1) What will be the performance of Original GAN and Incremental GAN by finding optimal weighting ( ) parameters for each of them? 2) Can you increase the number of parameters of the generator by no of generators used in the incremental GAN’s and compare the performance? 3) The abstract first line you have written ‘possibly distribution’ instead of ‘probability distribution’ 4) Table 1 ‘Incremental GAN’ doesn’t show consistently improved performance in comparison ‘Original GAN’. Can you train a few more generators and verify it?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Incremental training of multi-generative adversarial networks
Generative neural networks map a standard, possibly distribution to a complex high-dimensional distribution, which represents the real world data set. However, a determinate input distribution as well as a specific architecture of neural networks may impose limitations on capturing the diversity in the high dimensional target space. To resolve this difficulty, we propose a training framework that greedily produce a series of generative adversarial networks that incrementally capture the diversity of the target space. We show theoretically and empirically that our training algorithm converges to the theoretically optimal distribution, the projection of the real distribution onto the convex hull of the network*s distribution space.
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 method for ensembling GAN’s for capturing diversity in the target space. This done by a convex combination of GAN’s that are sequentially trained by trying to approximate the real distribution by fixing the previous generators. The paper theoretically shows that this approach converges to the optimal theoretical distribution. Comments 1) What will be the performance of Original GAN and Incremental GAN by finding optimal weighting ( ) parameters for each of them? 2) Can you increase the number of parameters of the generator by no of generators used in the incremental GAN’s and compare the performance? 3) The abstract first line you have written ‘possibly distribution’ instead of ‘probability distribution’ 4) Table 1 ‘Incremental GAN’ doesn’t show consistently improved performance in comparison ‘Original GAN’. Can you train a few more generators and verify it?
assistant
 This paper proposes a method for ensembling GAN’s for capturing diversity in the target space. This done by a convex combination of GAN’s that are sequentially trained by trying to approximate the real distribution by fixing the previous generators. The paper theoretically shows that this approach converges to the optimal theoretical distribution. Comments 1) What will be the performance of Original GAN and Incremental GAN by finding optimal weighting ( ) parameters for each of them? 2) Can you increase the number of parameters of the generator by no of generators used in the incremental GAN’s and compare the performance? 3) The abstract first line you have written ‘possibly distribution’ instead of ‘probability distribution’ 4) Table 1 ‘Incremental GAN’ doesn’t show consistently improved performance in comparison ‘Original GAN’. Can you train a few more generators and verify it?