Bridging the Gap Between f-GANs and Wasserstein GANs

ICML 2020

Bridging the Gap Between f-GANs and Wasserstein GANs

Jul 12, 2020
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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 other distances between distributions. Speakers: Jiaming Song, Stefano Ermon

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