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arXiv · 2102.06862

Wasserstein Proximal of GANs

Alex Tong Lin·Wuchen Li·Stanley Osher·Guido Montufar
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Abstract

We introduce a new method for training generative adversarial networks by applying the Wasserstein-2 metric proximal on the generators. The approach is based on Wasserstein information geometry. It defines a parametrization invariant natural gradient by pulling back optimal transport structures from probability space to parameter space. We obtain easy-to-implement iterative regularizers for the parameter updates of implicit deep generative models. Our experiments demonstrate that this method improves the speed and stability of training in terms of wall-clock time and Fr\'echet Inception Distance.

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Alex Tong Lin, Wuchen Li, Stanley Osher, Guido Montufar. 2021-02-13. Wasserstein Proximal of GANs. https://arxiv.org/abs/2102.06862

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