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While Generative Adversarial Networks (GANs) are fundamental to manygenerative modelling applications, they suffer from numerous issues. In thiswork, we propose a principled framework to simultaneously mitigate twofundamental issues in GANs: catastrophic forgetting of the discriminator andmode collapse of the generator. We achieve this by employing for GANs acontrastive learning and mutual information maximization approach, and performextensive analyses to understand sources of improvements. Our approachsignificantly stabilizes GAN training and improves GAN performance for imagesynthesis across five datasets under the same training and evaluationconditions against state-of-the-art works. In particular, compared to thestate-of-the-art SSGAN, our approach does not suffer from poorer performance onimage domains such as faces, and instead improves performance significantly.Our approach is simple to implement and practical: it involves only oneauxiliary objective, has a low computational cost, and performs robustly acrossa wide range of training settings and datasets without any hyperparametertuning. For reproducibility, our code is available in Mimicry:https://github.com/kwotsin/mimicry.

InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning