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.

提出 Adversarial Bayesian Augmentation（ABA）算法，该算法利用对抗学习和贝叶斯神经网络生成多样化的数据增强，以解决领域泛化中缺乏多样训练数据、目标数据不可访问和领域偏移等问题，通过在多种领域上生成合成图像域，帮助分类器在未见到的领域中泛化。在包括样式偏移、子人群偏移和医学图像处理等多种领域偏移情况下，ABA 超越了所有以往的最先进方法，包括预先指定的增强、基于像素和卷积的增强技术。