Joint covariate-alignment and concept-alignment: a framework for domain generalization

Domain generalization (DG) has been studied extensively over the past decade as an important practical problem arising in a number of areas such as computer vision, signal processing, and medical imaging [1] [2]. Like standard learning settings, DG aims to learn a model from several seen domains (training data) that can generalize well on an unseen domain (test data). However, in contrast to the standard setting where the test data is assumed to come from the same distribution as training data, in DG, the distribution of the test data is different, i.e., there is a presence of what is referred to as a distribution shift. This phenomena can be observed in many practical settings [3]. A number of approaches for DG are based on the assumption that there exist domain-invariant features that are transferable and unchanged from domain to domain. Thus, a classifier designed on top of these features will likely generalize well to the unseen domain.