2022/08/01 by Thuan Nguyen, Boyang Lyu, Nguyen, Thuan +7
Computer Science · Medicine · #Domain Adaptation and Few-Shot Learning #Text and Document Classification Technologies #Respiratory viral infections research
paper · pdf · doi:10.48550/arxiv.2208.00898
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.