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Twin Auxilary Classifiers GAN
Mingming Gong · Yanwu Xu · Chunyuan Li · Kun Zhang · Kayhan Batmanghelich

Tue Dec 10 05:10 PM -- 05:15 PM (PST) @ West Exhibition Hall C + B3

Conditional generative models enjoy significant progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN) that generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier. However, the diversity of the generated samples by AC-GAN tends to decrease as the number of classes increases. In this paper, we identify the source of low diversity issue theoretically and propose a practical solution to the problem. We show that the auxiliary classifier in AC-GAN imposes perfect separability, which is disadvantageous when the supports of the class distributions have significant overlap. To address the issue, we propose Twin Auxiliary Classifiers Generative Adversarial Net (TAC-GAN) that adds a new player that interacts with other players (the generator and the discriminator) in GAN. Theoretically, we demonstrate that our TAC-GAN can effectively minimize the divergence between generated and real data distributions. Extensive experimental results show that our TAC-GAN can successfully replicate the true data distributions on simulated data, and significantly improves the diversity of class-conditional image generation on real datasets.

Author Information

Mingming Gong (University of Melbourne)
Yanwu Xu (University of Pittsburgh)
Chunyuan Li (Microsoft Research)
Kun Zhang (CMU)
Kayhan Batmanghelich (University of Pittsburgh)

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