An Empirical Study of the Effects of Sample-Mixing Methods for Efficient Training of Generative Adversarial Networks

TL;DR

This study investigates the impact of sample-mixing methods like Mixup, CutMix, and SRMix on improving GAN training efficiency and sample quality, introducing a new formalism for applying these methods to GANs with saturated losses.

Contribution

It introduces a novel formalism for applying sample-mixing methods to GANs with saturated losses and demonstrates their effectiveness in enhancing generated image quality.

Findings

SRMix consistently improved FID scores across datasets.

Mixed-samples influence discriminator decisions and feature representations.

Different mixing patterns affect high- and low-level features in generated images.

Abstract

It is well-known that training of generative adversarial networks (GANs) requires huge iterations before the generator's providing good-quality samples. Although there are several studies to tackle this problem, there is still no universal solution. In this paper, we investigated the effect of sample mixing methods, that is, Mixup, CutMix, and newly proposed Smoothed Regional Mix (SRMix), to alleviate this problem. The sample-mixing methods are known to enhance the accuracy and robustness in the wide range of classification problems, and can naturally be applicable to GANs because the role of the discriminator can be interpreted as the classification between real and fake samples. We also proposed a new formalism applying the sample-mixing methods to GANs with the saturated losses which do not have a clear "label" of real and fake. We performed a vast amount of numerical experiments using LSUN and CelebA datasets. The results showed that Mixup and SRMix improved the quality of the generated images in terms of FID in most cases, in particular, SRMix showed the best improvement in most cases. Our analysis indicates that the mixed-samples can provide different properties from the vanilla fake samples, and the mixing pattern strongly affects the decision of the discriminators. The generated images of Mixup have good high-level feature but low-level feature is not so impressible. On the other hand, CutMix showed the opposite tendency. Our SRMix showed the middle tendency, that is, showed good high and low level features. We believe that our finding provides a new perspective to accelerate the GANs convergence and improve the quality of generated samples.