NOVEL AND EFFECTIVE PARALLEL MIX-GENERATOR GENERATIVE ADVERSARIAL NETWORKS
In this paper, we propose a mix-generator generative adversarial networks (PGAN) model that works in parallel by mixing multiple disjoint generators to approximate a complex real distribution. In our model, we propose an adjustment component that collects all the generated data points from the generators, learns the boundary between each pair of generators, and provides error to separate the support of each of the generated distributions. To overcome the instability in a multiplayer game, a shrinkage adjustment component method is introduced to gradually reduce the boundary between generators during the training procedure. To address the linearly growing training time problem in a multiple generators model, we propose a method to train the generators in parallel. This means that our work can be scaled up to large parallel computation frameworks. We present an efficient loss function for the discriminator, an effective adjustment component, and a suitable generator. We also show how to introduce the decay factor to stabilize the training procedure. We have performed extensive experiments on synthetic datasets, MNIST, and CIFAR-10. These experiments reveal that the error provided by the adjustment component could successfully separate the generated distributions and each of the generators can stably learn a part of the real distribution even if only a few modes are contained in the real distribution.
 The paper proposes to use multiple generators to fix mode collapse issue. The multiple generators are trained to be diverse. Each generator uses the reverse KL loss so that it models a single mode. One disadvantage is that it increases the number of networks (and hence the number of parameters). The paper needs some additional experiments to convincingly demonstrate the usefulness of the proposed method. Experiments on a challenging dataset with large number of classes (e.g. ImageNet as done by AC-GAN paper) would better illustrate the power of the method. AC-GAN paper: Conditional Image Synthesis with Auxiliary Classifier GANs https://arxiv.org/pdf/1610.09585.pdf The paper lacks clarity in some places and could use another round of editing/polishing.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
NOVEL AND EFFECTIVE PARALLEL MIX-GENERATOR GENERATIVE ADVERSARIAL NETWORKS
In this paper, we propose a mix-generator generative adversarial networks (PGAN) model that works in parallel by mixing multiple disjoint generators to approximate a complex real distribution. In our model, we propose an adjustment component that collects all the generated data points from the generators, learns the boundary between each pair of generators, and provides error to separate the support of each of the generated distributions. To overcome the instability in a multiplayer game, a shrinkage adjustment component method is introduced to gradually reduce the boundary between generators during the training procedure. To address the linearly growing training time problem in a multiple generators model, we propose a method to train the generators in parallel. This means that our work can be scaled up to large parallel computation frameworks. We present an efficient loss function for the discriminator, an effective adjustment component, and a suitable generator. We also show how to introduce the decay factor to stabilize the training procedure. We have performed extensive experiments on synthetic datasets, MNIST, and CIFAR-10. These experiments reveal that the error provided by the adjustment component could successfully separate the generated distributions and each of the generators can stably learn a part of the real distribution even if only a few modes are contained in the real distribution.
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 proposes to use multiple generators to fix mode collapse issue. The multiple generators are trained to be diverse. Each generator uses the reverse KL loss so that it models a single mode. One disadvantage is that it increases the number of networks (and hence the number of parameters). The paper needs some additional experiments to convincingly demonstrate the usefulness of the proposed method. Experiments on a challenging dataset with large number of classes (e.g. ImageNet as done by AC-GAN paper) would better illustrate the power of the method. AC-GAN paper: Conditional Image Synthesis with Auxiliary Classifier GANs https://arxiv.org/pdf/1610.09585.pdf The paper lacks clarity in some places and could use another round of editing/polishing.
assistant
 The paper proposes to use multiple generators to fix mode collapse issue. The multiple generators are trained to be diverse. Each generator uses the reverse KL loss so that it models a single mode. One disadvantage is that it increases the number of networks (and hence the number of parameters). The paper needs some additional experiments to convincingly demonstrate the usefulness of the proposed method. Experiments on a challenging dataset with large number of classes (e.g. ImageNet as done by AC-GAN paper) would better illustrate the power of the method. AC-GAN paper: Conditional Image Synthesis with Auxiliary Classifier GANs https://arxiv.org/pdf/1610.09585.pdf The paper lacks clarity in some places and could use another round of editing/polishing.