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Wasserstein Generative Adversarial Networks

2020-03-09 | |  109 |   46 |   0

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 lik 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.

上一篇：Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference

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