cGANs with Multi-Hinge Loss

Conditional GANs [29] (cGANs) are a type of GAN that use conditional information such as class labels to guide the training of the discriminator and the generator. Most frameworks of cGANs either augment a GAN by injecting (embedded) class information into the architecture of the real/fake discriminator [31], or add an auxiliary loss that is class based [36]. We place the class conditional structure at the forefront of the generative model by proposing a loss that ensures generator updates are always class specific. Rather than training a function that measures the information theoretic distance between the generative distribution and one target distribution, we generalize the successful hinge-loss [28] that has become an essential ingredient of many GANs [38, 7] to the multi-class setting and use it to train a single generator classifier pair [38]. While the canonical hinge loss made generator updates according to a class agnostic margin a real/fake discriminator learned [28], our multi-class hinge-loss GAN updates the generator according to many classification margins. With this modification, we are able to accelerate training and achieve state of the art Inception Scores on CIFAR10, CIFAR100, and STL10.