Variational Approaches for Auto-Encoding Generative Adversarial Networks

TL;DR

This paper introduces a novel variational approach to combine auto-encoders with GANs, leveraging hierarchical structures and synthetic likelihoods to improve generative modeling and prevent mode collapse.

Contribution

It develops a unified variational framework that integrates auto-encoders and GANs using synthetic likelihoods and implicit posteriors, enhancing generative performance.

Findings

Effective fusion of variational auto-encoders and GANs

Systematic quantitative assessment of the proposed method

Improved mode coverage and sample diversity

Abstract

Auto-encoding generative adversarial networks (GANs) combine the standard GAN algorithm, which discriminates between real and model-generated data, with a reconstruction loss given by an auto-encoder. Such models aim to prevent mode collapse in the learned generative model by ensuring that it is grounded in all the available training data. In this paper, we develop a principle upon which auto-encoders can be combined with generative adversarial networks by exploiting the hierarchical structure of the generative model. The underlying principle shows that variational inference can be used a basic tool for learning, but with the in- tractable likelihood replaced by a synthetic likelihood, and the unknown posterior distribution replaced by an implicit distribution; both synthetic likelihoods and implicit posterior distributions can be learned using discriminators. This allows us to develop a natural fusion of variational auto-encoders and generative adversarial networks, combining the best of both these methods. We describe a unified objective for optimization, discuss the constraints needed to guide learning, connect to the wide range of existing work, and use a battery of tests to systematically and quantitatively assess the performance of our method.