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Wasserstein GAN

Introduces WGAN, an alternative GAN training algorithm that improves training stability and eliminates mode collapse.

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Wasserstein GAN

By Martín Arjovsky, Soumith Chintala, Léon BottouarXiv.org
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Wasserstein GAN (WGAN) proposes a new algorithm as an alternative to traditional generative adversarial network training. Rather than the standard GAN objective, it is built around a formulation the authors show is sound as an optimization problem, and they provide extensive theoretical work highlighting deep connections to other distances between probability distributions.

The method improves the stability of learning and gets rid of problems such as mode collapse, while providing meaningful learning curves that are useful for debugging and hyperparameter searches. This made adversarial training more reliable and interpretable, addressing long-standing practical difficulties in training GANs.

Abstract

The paper introduces WGAN, an alternative to conventional GAN training. It improves the stability of learning, removes issues such as mode collapse, and yields meaningful learning curves useful for debugging and hyperparameter searches. The authors show that the corresponding optimization problem is sound and provide extensive theoretical work linking it to other distances between probability distributions.

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generative adversarial networkstraining stabilitymode collapseWasserstein distancegenerative models
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