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

• URL: http://arxiv.org/abs/2208.00898v1
• Date: Mon, 1 Aug 2022 14:39:35 GMT
• Title: Joint covariate-alignment and concept-alignment: a framework for domain generalization
• Authors: Thuan Nguyen, Boyang Lyu, Prakash Ishwar, Matthias Scheutz, and Shuchin Aeron
• Abstract summary: We propose a novel domain generalization framework based on a new upper bound to the risk on the unseen domain. 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.
• Score: 28.391072289529053
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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