In this paper, we propose a novel domain generalization (DG) framework based
on a new upper bound to the risk on the unseen domain. Particularly, our
framework proposes to jointly minimize both the covariate-shift as well as the
concept-shift between the seen domains for a better performance on the unseen
domain. While the proposed approach can be implemented via an arbitrary
combination of covariate-alignment and concept-alignment modules, in this work
we use well-established approaches for distributional alignment namely, Maximum
Mean Discrepancy (MMD) and covariance Alignment (CORAL), and use an Invariant
Risk Minimization (IRM)-based approach for concept alignment. Our numerical
results show that the proposed methods perform as well as or better than the
state-of-the-art for domain generalization on several data sets.

本文提出了一种新颖的领域泛化（DG）框架，该框架基于一种新的风险上限，旨在联合最小化在已见域之间的协变量转移和概念转移，以在未见域上获得更好的性能。在本文中，我们使用了分布对齐的常用方法，即最大均值差异（MMD）和协方差对齐（CORAL），并使用基于不变风险最小化（IRM）的方法来进行概念对齐。我们的数值结果表明，所提出的方法在多个数据集上表现不输于领域泛化的最新技术。