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.
@inproceedings{nguyenetal2022mlsp,
title={Joint covariate-alignment and concept-alignment: a framework for domain generalization},
author={Thuan Nguyen, Boyang Lyu, Prakash Ishwar, Matthias Scheutz, Shuchin Aeron},
year={2022},
booktitle={32nd International Workshop on Machine Learning for Signal Processing (MLSP)},
url={https://hrilab.tufts.edu/publications/nguyenetal2022mlsp.pdf}
}