cGANs with Multi-Hinge Loss
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We propose a new algorithm to incorporate class conditional information into the discriminator of GANs via a multi-class generalization of the commonly used Hinge loss. Our approach is in contrast to most GAN frameworks in that we train a single classifier for K+1 classes with one loss function, instead of a real/fake discriminator, or a discriminator classifier pair. We show that learning a single good classifier and a single state of the art generator simultaneously is possible in supervised and semi-supervised settings. With our multi-hinge loss modification we were able to improve the state of the art CIFAR10 IS FID to 9.58 6.40, CIFAR100 IS FID to 14.36 13.32, and STL10 IS FID to 12.16 17.44. The code written with PyTorch is available at https://github.com/ilyakava/BigGAN-PyTorch.
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