Variational annealing of GANs: A Langevin perspective

Chenyang Tao, Shuyang Dai, Liqun Chen, Ke Bai, Junya Chen, Chang Liu, Ruiyi Zhang, Georgiy Bobashev, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The generative adversarial network (GAN) has received considerable attention recently as a model for data synthesis, without an explicit specification of a likelihood function. There has been commensurate interest in leveraging likelihood estimates to improve GAN training. To enrich the understanding of this fast-growing yet almost exclusively heuristic-driven subject, we elucidate the theoretical roots of some of the empirical attempts to stabilize and improve GAN training with the introduction of likelihoods. We highlight new insights from variational theory of diffusion processes to derive a likelihood-based regularizing scheme for GAN training, and present a novel approach to train GANs with an unnormalized distribution instead of empirical samples. To substantiate our claims, we provide experimental evidence on how our theoretically-inspired new algorithms improve upon current practice.
Original languageEnglish (US)
Title of host publication36th International Conference on Machine Learning, ICML 2019
PublisherInternational Machine Learning Society (IMLS)rasmussen@ptd.net
Pages10777-10786
Number of pages10
ISBN (Print)9781510886988
StatePublished - Jan 1 2019
Externally publishedYes

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