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.

翻译：鉴于生成对抗网络（GANs）在数据生成方面的卓越能力，其在无监督学习领域引起了广泛关注。然而，由于判别器的训练分布具有动态性，导致图像表示不稳定，使得GANs的训练较为困难。本文从一个新颖的视角——即鲁棒图像分类——来解决GANs的训练问题。受鲁棒图像表示研究的启发，我们提出了一个简单而有效的模块，称为AdaptiveMix，用于GANs。该模块通过收缩判别器图像表示空间中训练数据的区域。考虑到直接约束特征空间难以实现，我们提出了构建困难样本并缩小困难样本与易样本之间的特征距离的方法。通过混合一对训练图像来构建困难样本。我们使用广泛使用的先进GAN架构评估了AdaptiveMix的有效性。评估结果表明，我们的AdaptiveMix能够促进GANs的训练，并有效提升生成样本的图像质量。我们进一步展示了AdaptiveMix可以通过结合最先进的方法应用于图像分类和分布外（OOD）检测任务。在七个公开数据集上的大量实验表明，我们的方法有效提升了基线方法的性能。代码开源在：https://github.com/WentianZhang-ML/AdaptiveMix。