Neural Information Processing Systems http://nips.cc/

Generative adversarial networks (GANs) are a technique for learning generative models of complex data distributions from samples. Despite remarkable advances in generating realistic images, a major shortcoming of GANs is the fact that they tend to produce samples with little diversity, even when trained on diverse datasets. This phenomenon, known as mode collapse, has been the focus of much recent work. We study a principled approach to handling mode collapse, which we call packing. The main idea is to modify the discriminator to make decisions based on multiple samples from the same class, either real or artificially generated. We draw analysis tools from binary hypothesis testing---in particular the seminal result of Blackwell---to prove a fundamental connection between packing and mode collapse. We show that packing naturally penalizes generators with mode collapse, thereby favoring generator distributions with less mode collapse during the training process. Numerical experiments on benchmark datasets suggest that packing provides significant improvements.

Neural Information Processing Systems http://nips.cc/

We would like to thank all reviewers evaluating the paper, and will fully address all the review concerns in the revision. We have recently tested VGG face using 2000 classes. The FID score of T AC-GAN and PcGAN are 13.79 and 22.42. This indicates that the drawbacks in AC-GAN loss cannot be fully addressed by pacGAN. We would like to thank R#2 for very detailed comments.

Originality: The main originality of the paper is a data transformation process applied to tabular data so a GAN can learn from them. This is definitely higher novel and can be potentially useful in similar situations involving such distributions. Apart from this, however, I feel that the authors are overclaiming a bit regarding several challenge/contributions: -C2 (L86): The choice of activation function certainly depends on the data format, listing that as a "challenge" seems a bit too much to me, unless the authors can point out non-trivial adaptations they made to address the problem (and apologize if I missed that...) -C4 (L98): again, hardly something new -C5 (L105): mode collapse is certainly well studied in literature (speaking of which, the authors should add references on newer approaches such as BourGAN), using an off-the-shelf solution (PacGAN), again, does not seem to me as an important contribution. Rephrasing the section and focus on the important contributions (C3, and perhaps C1) will make the contributions of the paper more clear, in my opinion. Quality: The paper is of high quality and the description of techniques is sound.

This paper proposes a new metric for mode collapse, which is a scalar value that can be read off from previously proposed measure of mode collapse in PacGAN. Precisely, in the mode collapse region, one can read the two points: (i) where the mode collapse region touches vertical axis ( \delta -axis) and (ii) where the mode collapse r region touches \delta 1 line. Each one is exactly the same as P_r(support{P_g}) and P_g(support{P_r}) that defend the proposed scalar valued mode collapse measure. This should be explained precisely in the paper, as (i) PacGAN introduced a proper mathematical notion of mode collapse earlier, (ii) the mode collapse region strictly generalizes the proposed metric (iii) mode collapse regions is the foundation of understanding mode collapse theoretically. A new estimator based on nearest neighbor distances are proposed, with extensive numerical validation of the proposed metric.

Summary: While Generative Adversarial Networks (GANs) have become the desired choice for generative tasks in the community, they also suffer from a nagging issue of mode collapse (cf. The current literature also has some empirical ways to handle this issue (cf. They present the technique of packing, in which the discriminator now uses multiple samples in its task. Detailed Comments: Clarity: The paper is very well written, both rigor and intuitive expositions are presented. Originality: As explained above in summary, perhaps this is the first time a framework of mode collapse is constructed and its theoretical underpinnings are discussed.