Wasserstein Generative Adversarial Networks

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Martin Arjovsky, Soumith Chintala, Léon Bottou ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:214-223, 2017.

Abstract

We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to different distances between distributions.

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