Restricting Greed in Training of Generative Adversarial Network

TL;DR

This paper introduces a novel training strategy for GANs that restricts greediness during training, leading to more diverse generated data and more stable training processes across various datasets.

Contribution

The paper proposes a new method to limit greed in GAN training, improving diversity and stability compared to traditional approaches.

Findings

Generated data covers more modes of real data.

Training process is more stable.

Method outperforms traditional GAN training on multiple datasets.

Abstract

Generative adversarial network (GAN) has gotten wide re-search interest in the field of deep learning. Variations of GAN have achieved competitive results on specific tasks. However, the stability of training and diversity of generated instances are still worth studying further. Training of GAN can be thought of as a greedy procedure, in which the generative net tries to make the locally optimal choice (minimizing loss function of discriminator) in each iteration. Unfortunately, this often makes generated data resemble only a few modes of real data and rotate between modes. To alleviate these problems, we propose a novel training strategy to restrict greed in training of GAN. With help of our method, the generated samples can cover more instance modes with more stable training process. Evaluating our method on several representative datasets, we demonstrate superiority of improved training strategy on typical GAN models with different distance metrics.