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Search results with tag "Wasserstein"

Wasserstein Generative Adversarial Networks

Wasserstein Generative Adversarial Networks

proceedings.mlr.press

-space, since it’s easier for the distributions to converge. The main reason we care about the mapping 7!P to be continuous is as follows. If ˆis our notion of distance be-tween two distributions, we would like to have a loss func-tion 7!ˆ(P ;P r)that is continuous, and this is equivalent to having the mapping 7!P be continuous when using

  Wasserstein

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