  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.
