Joint covariate-alignment and concept-alignment: a framework for domain generalization
This addresses the problem of domain generalization for machine learning models, but it is incremental as it combines existing alignment methods.
The paper tackles domain generalization by proposing a framework that jointly minimizes covariate-shift and concept-shift between seen domains to improve performance on unseen domains, achieving results that match or exceed state-of-the-art on several datasets.
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.