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Bayesian GANs and Stochastic MCMC

Banff International Research Station for Mathematical Innovation and Discovery

Through an adversarial game, generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. I will present a practical Bayesian formulation for unsupervised and semi-supervised learning with GANs. Within this framework, we use stochastic gradient Hamiltonian Monte Carlo for marginalizing parameters. The resulting approach can automatically discover complementary and interpretable generative hypotheses for collections of images. Moreover, by exploring an expressive posterior over these hypotheses, we show that it is possible to achieve state-of-the-art quantitative results on image classification benchmarks, even with less than 1% of the labelled training data.

Mathematics; Statistics; Computer science; Data mining

video/mp4

Author affiliation: Cornell University

2018-07-17

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/66514

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/