Omni-GAN: On the Secrets of cGANs and Beyond

Peng Zhou, Lingxi Xie, Bingbing Ni, Qi Tian

It has been an important problem to design a proper discriminator for conditional generative adversarial networks (cGANs). In this paper, we investigate two popular choices, the projection-based and classification-based discriminators, and reveal that both of them suffer some kind of drawbacks that affect the learning ability of cGANs. Then, we present our solution that trains a powerful discriminator and avoids over-fitting with regularization. In addition, we unify multiple targets (class, domain, reality, etc.) into one loss function to enable a wider range of applications. Our algorithm, named \textbf{Omni-GAN}, achieves competitive performance on a few popular benchmarks. More importantly, Omni-GAN enjoys both high generation quality and low risks in mode collapse, offering new possibilities for optimizing cGANs.Code is available at \url{https://github.com/PeterouZh/Omni-GAN-PyTorch}.

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