Bridging Maximum Likelihood and Adversarial Learning via $\alpha$-Divergence

TL;DR

This paper introduces an $oldsymbol{ extalpha}$-Bridge that unifies maximum likelihood and adversarial learning for generative models, leveraging $oldsymbol{ extalpha}$-divergence to combine their strengths and improve training stability and image quality.

Contribution

The authors propose an $oldsymbol{ extalpha}$-Bridge method that smoothly transitions between ML and adversarial learning using $oldsymbol{ extalpha}$-divergence, providing new insights into regularization and training stability.

Findings

The $oldsymbol{ extalpha}$-Bridge effectively combines ML and adversarial learning benefits.

Generalizations of the $oldsymbol{ extalpha}$-Bridge relate to recent regularization techniques.

The approach improves training stability and image realism in generative models.

Abstract

Maximum likelihood (ML) and adversarial learning are two popular approaches for training generative models, and from many perspectives these techniques are complementary. ML learning encourages the capture of all data modes, and it is typically characterized by stable training. However, ML learning tends to distribute probability mass diffusely over the data space, $e.g.$, yielding blurry synthetic images. Adversarial learning is well known to synthesize highly realistic natural images, despite practical challenges like mode dropping and delicate training. We propose an $\alpha$-Bridge to unify the advantages of ML and adversarial learning, enabling the smooth transfer from one to the other via the $\alpha$-divergence. We reveal that generalizations of the $\alpha$-Bridge are closely related to approaches developed recently to regularize adversarial learning, providing insights into that prior work, and further understanding of why the $\alpha$-Bridge performs well in practice.