Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models

Understand

Adversarial learning of probabilistic models has recently emerged as a promising alternative to maximum likelihood.

• Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum likelihood.
• Yet, GANs sidestep the characterization of an explicit density which makes quantitative evaluations challenging.
• To bridge this gap, we propose Flow-GANs, a generative adversarial network for which we can perform exact likelihood evaluation, thus supporting both adversarial and maximum likelihood training.