Timezone: »

 
Spotlight
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
in Track 1 Session 2 »

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)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors