Self-supervised (SS) learning is a powerful approach for representation learning using unlabeled data. Recently, it has been applied to Generative Adversarial Networks (GAN) training. Specifically, SS tasks were proposed to address the catastrophic forgetting issue in the GAN discriminator. In this work, we perform an in-depth analysis to understand how SS tasks interact with learning of generator. From the analysis, we identify issues of SS tasks which allow a severely mode-collapsed generator to excel the SS tasks.

Originality: The method is relatively new although it is similar to some conditional GAN works in the literature. The main idea is the analysis showing the limitations of prior GAN+SSL work and in proposing a scheme with better chances of succeeding (at least theoretically). Then experiments show that there is an improvement. It would be good to show more the analogies to prior conditional GAN work, and this would not hurt the contribution, rather it would better clarify its context and provide more links to practitioners (who could better understand it). Basically, the minimax game should use the same cost function for the optimization of the discriminator, the generator and the classifier.

NeurIPS 2019 Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center "7259" "Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game" The paper addresses a problem in self supervised GAN, where the classes strictly have disjoint support. This is mitigated by introducing a new class for generated samples.

Self-supervised (SS) learning is a powerful approach for representation learning using unlabeled data. Recently, it has been applied to Generative Adversarial Networks (GAN) training. Specifically, SS tasks were proposed to address the catastrophic forgetting issue in the GAN discriminator. In this work, we perform an in-depth analysis to understand how SS tasks interact with learning of generator. From the analysis, we identify issues of SS tasks which allow a severely mode-collapsed generator to excel the SS tasks.

Self-supervised (SS) learning is a powerful approach for representation learning using unlabeled data. Recently, it has been applied to Generative Adversarial Networks (GAN) training. Specifically, SS tasks were proposed to address the catastrophic forgetting issue in the GAN discriminator. In this work, we perform an in-depth analysis to understand how SS tasks interact with learning of generator. From the analysis, we identify issues of SS tasks which allow a severely mode-collapsed generator to excel the SS tasks.