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Bayesian Generative Adversarial Networks

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  • UserProfessor Andrew Wilson, Cornell University
  • ClockWednesday 13 December 2017, 11:00-12:00
  • HouseCBL Seminar Room.

If you have a question about this talk, please contact Pat Wilson.

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 a stochastic gradient Hamiltonian Monte Carlo for marginalizing parameters. The resulting approach can automatically discover complementary and interpretable generative hypotheses for collections of images, without ad hoc interventions. 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.

This talk is part of the Machine Learning @ CUED series.

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