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Wasserstein Proximal of GANs

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 نشر من قبل Alex Tong Lin
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
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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 Frechet Inception Distance.



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