Bayesian GANs

The paper Bayesian GAN by Y. Saatchi and A.G. Wilson introduces a new formulation for GANs that applies Bayesian techniques, and outperforms some of the current state-of-the-art approaches. It is a true generalization in that we can recover the original GAN formulation given a specific choice of parameters, but authors also show that this formulation allows to sample from a family of generator and discriminators which avoids mode collapse. Posteriors on the parameters of the generator and the disciminator are defined mathematically as a marginalization over the noise inputs. An algorithm is presented to sample from the posterior distributions defining generators and discriminators, using SGHMC and MC.

We will first explain the intuition behind the paper, describe the most important mathematical underpinnings and apply the algorithm to a new, simple problem. Finally, we extend the model to a new, unreleased dataset and show how it performs in comparison to other state of the art methods.

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