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Wasserstein GAN
Wasserstein GANM artin Arjovsky1, Soumith Chintala2, and L eon Bottou1,21 Courant Institute of Mathematical Sciences2 Facebook AI Research1 IntroductionThe problem this paper is concerned with is that of unsupervised learning. Mainly,what does it mean to learn a probability distribution? The classical answer to thisis to learn a probability density. This is often done by defining a parametric familyof densities (P ) Rdand finding the one that maximized the likelihood on our data:if we have real data examples{x(i)}mi=1, we would solve the problemmax Rd1mm i=1logP (x(i))If the real data distributionPradmits a density andP is the distribution of theparametrized densityP , then, asymptotically, this amounts to minimizing theKullback-Leibler divergenceKL(Pr P ).For this to make sense, we need the model densityP to exist. This is notthe case in the rather common situation where we are dealing with distributionssupported by low dimensional manifolds.
Wasserstein GAN Martin Arjovsky1, Soumith Chintala2, and L eon Bottou1,2 1Courant Institute of Mathematical Sciences 2Facebook AI Research 1 Introduction The problem this paper is concerned with is that of unsupervised learning. Mainly, what does it …
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