Bidirectional Generative Modeling Using Adversarial Gradient Estimation

TL;DR

This paper introduces a unified adversarial gradient estimation method for bidirectional generative models based on $f$-divergences, encompassing VAE and BiGAN, with theoretical and empirical validation.

Contribution

It proposes a new optimization technique using adversarially learned discriminators for $f$-divergence based models, providing a general framework and analysis.

Findings

The method effectively estimates gradients for various divergences.

The approach outperforms existing methods in empirical evaluations.

Different divergences lead to similar algorithms with scaled gradients.

Abstract

This paper considers the general $f$-divergence formulation of bidirectional generative modeling, which includes VAE and BiGAN as special cases. We present a new optimization method for this formulation, where the gradient is computed using an adversarially learned discriminator. In our framework, we show that different divergences induce similar algorithms in terms of gradient evaluation, except with different scaling. Therefore this paper gives a general recipe for a class of principled $f$-divergence based generative modeling methods. Theoretical justifications and extensive empirical studies are provided to demonstrate the advantage of our approach over existing methods.