  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.
