Generative adversarial nets (GANs) are good at generating realistic images and have been extended for semi-supervised classification. However, under a two-player formulation, existing work shares competing roles of identifying fake samples and predicting labels via a single discriminator network, which can lead to undesirable incompatibility. We present triple generative adversarial net (Triple-GAN), a flexible game-theoretical framework for classification and class-conditional generation in semi-supervised learning. Triple-GAN consists of three players - a generator, a discriminator and a classifier, where the generator and classifier characterize the conditional distributions between images and labels, and the discriminator solely focuses on identifying fake image-label pairs. With designed utilities, the distributions characterized by the classifier and generator both concentrate to the data distribution under nonparametric assumptions. We further propose unbiased regularization terms to make the classifier and generator strongly coupled and some biased techniques to boost the performance of Triple-GAN in practice. Our results on several datasets demonstrate the promise in semi-supervised learning, where Triple-GAN achieves comparable or superior performance than state-of-the-art classification results among DGMs; it is also able to disentangle the classes and styles and transfer smoothly on the data level via interpolation on the latent space class-conditionally.

本文提出了一种三角对抗生成网络，它包含三个玩家（生成器、判别器和分类器），生成器和分类器特征化图像和标签之间的条件分布，而判别器仅关注辨别假的图像-标签对，并且设计兼容的实用程序以确保分类器和生成器特征化的分布都收敛于数据分布。

三重生成对抗网络