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In this paper, we propose a novel domain generalization (DG) framework basedon a new upper bound to the risk on the unseen domain. Particularly, ourframework proposes to jointly minimize both the covariate-shift as well as theconcept-shift between the seen domains for a better performance on the unseendomain. While the proposed approach can be implemented via an arbitrarycombination of covariate-alignment and concept-alignment modules, in this workwe use well-established approaches for distributional alignment namely, MaximumMean Discrepancy (MMD) and covariance Alignment (CORAL), and use an InvariantRisk Minimization (IRM)-based approach for concept alignment. Our numericalresults show that the proposed methods perform as well as or better than thestate-of-the-art for domain generalization on several data sets.

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