Tempered Adversarial Networks

TL;DR

This paper introduces a simple, generic modification to GAN training that balances the learning process by controlling the real data exposure, leading to improved stability and quality across various GAN architectures.

Contribution

A novel tempered learning approach that adjusts the exposure of real data to the discriminator, enhancing GAN training stability and performance.

Findings

Improved stability across multiple GAN variants.

Faster convergence in training.

Enhanced quality of generated samples.

Abstract

Generative adversarial networks (GANs) have been shown to produce realistic samples from high-dimensional distributions, but training them is considered hard. A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fake samples, the generator only has control over the fake samples it produces since the real data distribution is fixed by the choice of a given dataset. We propose a simple modification that gives the generator control over the real samples which leads to a tempered learning process for both generator and discriminator. The real data distribution passes through a lens before being revealed to the discriminator, balancing the generator and discriminator by gradually revealing more detailed features necessary to produce high-quality results. The proposed module automatically adjusts the learning process to the current strength of the networks, yet is generic and easy to add to any GAN variant. In a number of experiments, we show that this can improve quality, stability and/or convergence speed across a range of different GAN architectures (DCGAN, LSGAN, WGAN-GP).