Taming Mode Collapse in Generative Adversarial Networks Using Cooperative Realness Discriminators
Abstract: Generative adversarial networks (GANs) are able to produce realistic images. However, GANs may suffer mode collapse in their output data distribution. Here, we theoretically and empirically justify generalizing the GAN framework to multiple discriminators with one generator for improving generative performance. First, we adopt a comprehensive perspective to understand why mode collapse occurs. Second, we introduce an array of cooperative realness discriminators into the GAN framework to combat mode collapse and explore discriminator roles ranging from a formidable adversary to a forgiving teacher. Third, we propose two types of simple yet effective regularization for generating realistic and diverse images. Experiments on various datasets show the effectiveness of our GAN compared to previous methods in alleviating mode collapse and improving the quality of the generated samples.