Autoencoding Generative Adversarial Networks

TL;DR

The paper introduces AEGAN, a four-network model that enhances GAN training stability, prevents mode collapse, and enables direct interpolation between real samples by learning a bijective mapping between latent and sample spaces.

Contribution

It proposes the AEGAN model, which combines adversarial and reconstruction losses to improve GAN training stability and sample interpolation capabilities.

Findings

Improved training stability over traditional GANs

Prevents mode collapse effectively

Enables direct interpolation between real samples

Abstract

In the years since Goodfellow et al. introduced Generative Adversarial Networks (GANs), there has been an explosion in the breadth and quality of generative model applications. Despite this work, GANs still have a long way to go before they see mainstream adoption, owing largely to their infamous training instability. Here I propose the Autoencoding Generative Adversarial Network (AEGAN), a four-network model which learns a bijective mapping between a specified latent space and a given sample space by applying an adversarial loss and a reconstruction loss to both the generated images and the generated latent vectors. The AEGAN technique offers several improvements to typical GAN training, including training stabilization, mode-collapse prevention, and permitting the direct interpolation between real samples. The effectiveness of the technique is illustrated using an anime face dataset.