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Wasserstein GAN
lib:195e9242e1ae71c5 (v1.0.0)
Authors: Martin Arjovsky,Soumith Chintala,Léon Bottou
ArXiv: 1701.07875
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1701.07875v3
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.
ArXiv: 1701.07875
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1701.07875v3
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.
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