Folded Hamiltonian Monte Carlo for Bayesian Generative Adversarial Networks

Narges Pourshahrokhi, Yunpeng Li, Samaneh Kouchaki, Payam M. Barnaghi

Abstract: Probabilistic modelling on Generative Adversarial Networks (GANs) within the Bayesian framework has shown success in estimating the complex distribution in literature. In this paper, we develop a Bayesian formulation for unsupervised and semi-supervised GAN learning. Specifically, we propose Folded Hamiltonian Monte Carlo (F-HMC) methods within this framework to learn the distributions over the parameters of the generators and discriminators. We show that the F-HMC efficiently approximates multi-modal and high dimensional data when combined with Bayesian GANs. Its composition improves run time and test error in generating diverse samples. Experimental results with high-dimensional synthetic multi-modal data and natural image benchmarks, including CIFAR-10, SVHN and ImageNet, show that F-HMC outperforms the state-of-the-art methods in terms of test error, run times per epoch, inception score and Frechet Inception Distance scores.