  Due to the outstanding capability for data generation, Generative Adversarial
Networks (GANs) have attracted considerable attention in unsupervised learning.
However, training GANs is difficult, since the training distribution is dynamic
for the discriminator, leading to unstable image representation. In this paper,
we address the problem of training GANs from a novel perspective, \emph{i.e.,}
robust image classification. Motivated by studies on robust image
representation, we propose a simple yet effective module, namely AdaptiveMix,
for GANs, which shrinks the regions of training data in the image
representation space of the discriminator. Considering it is intractable to
directly bound feature space, we propose to construct hard samples and narrow
down the feature distance between hard and easy samples. The hard samples are
constructed by mixing a pair of training images. We evaluate the effectiveness
of our AdaptiveMix with widely-used and state-of-the-art GAN architectures. The
evaluation results demonstrate that our AdaptiveMix can facilitate the training
of GANs and effectively improve the image quality of generated samples. We also
show that our AdaptiveMix can be further applied to image classification and
Out-Of-Distribution (OOD) detection tasks, by equipping it with
state-of-the-art methods. Extensive experiments on seven publicly available
datasets show that our method effectively boosts the performance of baselines.
The code is publicly available at
https://github.com/WentianZhang-ML/AdaptiveMix.
