Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy

14 Nov 2016Dougal J. SutherlandHsiao-Yu TungHeiko StrathmannSoumyajit DeAaditya RamdasAlex SmolaArthur Gretton

We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test based on the maximum mean discrepancy (MMD). This optimized MMD is applied to the setting of unsupervised learning by generative adversarial networks (GAN), in which a model attempts to generate realistic samples, and a discriminator attempts to tell these apart from data samples... (read more)

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