cGANs with Multi-Hinge Loss

9 Dec 2019  ·  Ilya Kavalerov, Wojciech Czaja, Rama Chellappa ·

We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised settings. We study the compromise between training a state of the art generator and an accurate classifier simultaneously, and propose a way to use our algorithm to measure the degree to which a generator and critic are class conditional. We show the trade-off between a generator-critic pair respecting class conditioning inputs and generating the highest quality images. With our multi-hinge loss modification we are able to improve Inception Scores and Frechet Inception Distance on the Imagenet dataset. We make our tensorflow code available at

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Conditional Image Generation CIFAR-10 MHingeGAN Inception score 9.58 # 4
FID 7.5 # 8
Conditional Image Generation CIFAR-100 MHingeGAN Inception Score 14.36 # 1
FID 17.3 # 6