Generalizing to unseen image domains is a challenging problem primarily due to the lack of diverse training data, inaccessible target data, and the large domain shift that may exist in many real-world settings. As such data augmentation is a critical component of domain generalization methods that seek to address this problem. We present Adversarial Bayesian Augmentation (ABA), a novel algorithm that learns to generate image augmentations in the challenging single-source domain generalization setting. ABA draws on the strengths of adversarial learning and Bayesian neural networks to guide the generation of diverse data augmentations -- these synthesized image domains aid the classifier in generalizing to unseen domains. We demonstrate the strength of ABA on several types of domain shift including style shift, subpopulation shift, and shift in the medical imaging setting. ABA outperforms all previous state-of-the-art methods, including pre-specified augmentations, pixel-based and convolutional-based augmentations.

翻译：泛化至未见图像域是一项极具挑战性的问题，主要源于训练数据多样性不足、目标域数据不可获取以及现实场景中可能存在的巨大域偏移。因此，数据增强作为域泛化方法的关键组成部分，旨在解决该问题。本文提出对抗贝叶斯增强（Adversarial Bayesian Augmentation, ABA）——一种在具有挑战性的单源域泛化设定中学习生成图像增强的新算法。ABA融合了对抗学习与贝叶斯神经网络的各自优势，引导生成多样化的数据增强——这些合成图像域有助于分类器泛化至未见领域。我们在包括风格偏移、子群体偏移及医学影像设定下的域偏移等多种域偏移类型上展示了ABA的优异性能。ABA超越了包括预定义增强、基于像素及基于卷积增强在内的所有先前最优方法。