Reviews: MarginGAN: Adversarial Training in Semi-Supervised Learning

The main contribution of this paper is in setting up a 3 player game for semi-supervised learning where the generator tries to maximize the margin of the examples it generates in competition with a classifier the traditional GAN approach of fooling a discriminator. This idea is novel to my knowledge. One small reservation I have with this method is that as the quality of the GAN and generated images increases the margin maximization for the classifier for generated examples becomes counter productive (as acknowledged by the authors) which requires careful early stopping. But this is standard practice with GANs and it should not be held against this paper. The paper is generally of high quality and significance but these could be improved by a broader treatment of related works.