Transcription of Wasserstein Generative Adversarial Networks
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Wasserstein Generative Adversarial Networks
Wasserstein Generative Adversarial NetworksMartin Arjovsky1 Soumith Chintala2L eon Bottou1 2 AbstractWe introduce a new algorithm named WGAN,an alternative to traditional GAN training. Inthis new model, we show that we can improvethe stability of learning, get rid of problems likemode collapse, and provide meaningful learningcurves useful for debugging and hyperparametersearches. Furthermore, we show that the cor-responding optimization problem is sound, andprovide extensive theoretical work highlightingthe deep connections to different distances be-tween IntroductionThe problem this paper is concerned with is that of unsu-pervised learning.
Wasserstein Generative Adversarial Networks Figure 1: These plots show ˆ(P ;P 0) as a function of when ˆis the EM distance (left plot) or the JS divergence (right plot).The EM plot is continuous and provides a usable gradient everywhere.
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