Combining Data Augmentation and Domain Distance Minimisation to Reduce Domain Generalisation Error

Abstract

Domain generalisation represents the challenging problem of using multiple training domains to learn a model that can generalise to previously unseen target domains. Recent papers have proposed using data augmentation to produce realistic adversarial examples to simulate domain shift. Under current domain adaptation/generalisation theory, it is unclear whether training with data augmentation alone is sufficient to improve domain generalisation results. We propose an extension of the current domain generalisation theoretical framework and a new method that combines data augmentation and domain distance minimisation to reduce the upper bound on domain generalisation error. Empirically, our algorithm produces competitive results when compared with the state-of-the-art methods in the domain generalisation benchmark PACS. We have also performed an ablation study of the technique on a real-world chest x-ray dataset, consisting of a subset of CheXpert, Chest14, and PadChest datasets. The result shows that the proposed method works best when the augmented domains are realistic, but it can perform robustly even when domain augmentation fails to produce realistic samples. different architectures and loss functions on CIFAR-10 dataset.