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Recently, semi-supervised learning methods based on generative adversarialnetworks (GANs) have received much attention. Among them, two distinctapproaches have achieved competitive results on a variety of benchmarkdatasets. Bad GAN learns a classifier with unrealistic samples distributed onthe complement of the support of the input data. Conversely, Triple GANconsists of a three-player game that tries to leverage good generated samplesto boost classification results. In this paper, we perform a comprehensivecomparison of these two approaches on different benchmark datasets. Wedemonstrate their different properties on image generation, and sensitivity tothe amount of labeled data provided. By comprehensively comparing these twomethods, we hope to shed light on the future of GAN-based semi-supervisedlearning.

Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach