Metropolis-Hastings Generative Adversarial Networks

    Abstract

    We introduce the Metropolis-Hastings generative adversarial network (MH-GAN), which combines aspects of Markov chain Monte Carlo and GANs. The MH-GAN draws samples from the distribution implicitly defined by a GAN’s discriminator-generator pair, as opposed to sampling in a standard GAN which draws samples from the distribution defined by the generator. It uses the discriminator from GAN training to build a wrapper around the generator for improved sampling. With a perfect discriminator, this wrapped generator samples from the true distribution on the data exactly even when the generator is imperfect. We demonstrate the benefits of the improved generator on multiple benchmark datasets, including CIFAR-10 and CelebA, using DCGAN and WGAN.

    Authors

    Ryan Turner, Jane Hung, Yunus Saatci, Jason Yosinski

    Conference

    ICML 2019

    Full Paper

    ‘Metropolis-Hastings Generative Adversarial Networks’ (PDF)

    Uber AI

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