Co-generation with GANs using AIS based HMC

Inferring the most likely configuration for a subset of variables of a joint distribution given the remaining ones – which we refer to as co-generation – is an important challenge that is computationally demanding for all but the simplest settings. This task has received a considerable amount of attention, particularly for classical ways of modeling distributions like structured prediction. In contrast, almost nothing is known about this task when considering recently proposed techniques for modeling high-dimensional distributions, particularly generative adversarial nets (GANs). Therefore, in this paper, we study the occurring challenges for co-generation with GANs. To address those challenges we develop an annealed importance sampling based Hamiltonian Monte Carlo co-generation algorithm. The presented approach significantly outperforms classical gradient based methods on a synthetic and on the CelebA and LSUN datasets.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Fang, Tiantian

Contributors dc:contributor

• Schwing, Alexander

• Co-Generation, generative model, annealed importance sampling

Statement dc:rights

• Copyright 2020 Tiantian Fang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108187

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108187