Improving GAN Training via Feature Space Shrinkage

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, i.e., robust image classification. Motivated by studies on robust image representation, we propose a simple yet effective module, namely <PRE_TAG>AdaptiveMix</POST_TAG>, 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 <PRE_TAG>AdaptiveMix</POST_TAG> with widely-used and state-of-the-art GAN architectures. The evaluation results demonstrate that our <PRE_TAG>AdaptiveMix</POST_TAG> can facilitate the training of GANs and effectively improve the image quality of generated samples. We also show that our <PRE_TAG>AdaptiveMix</POST_TAG> 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/<PRE_TAG>AdaptiveMix</POST_TAG>.