Domain Generalization via Rationale Invariance
- URL: http://arxiv.org/abs/2308.11158v1
- Date: Tue, 22 Aug 2023 03:31:40 GMT
- Title: Domain Generalization via Rationale Invariance
- Authors: Liang Chen, Yong Zhang, Yibing Song, Anton van den Hengel, and
Lingqiao Liu
- Abstract summary: This paper offers a new perspective to ease the challenge of domain generalization, which involves maintaining robust results even in unseen environments.
We propose treating the element-wise contributions to the final results as the rationale for making a decision and representing the rationale for each sample as a matrix.
Our experiments demonstrate that the proposed approach achieves competitive results across various datasets, despite its simplicity.
- Score: 70.32415695574555
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper offers a new perspective to ease the challenge of domain
generalization, which involves maintaining robust results even in unseen
environments. Our design focuses on the decision-making process in the final
classifier layer. Specifically, we propose treating the element-wise
contributions to the final results as the rationale for making a decision and
representing the rationale for each sample as a matrix. For a well-generalized
model, we suggest the rationale matrices for samples belonging to the same
category should be similar, indicating the model relies on domain-invariant
clues to make decisions, thereby ensuring robust results. To implement this
idea, we introduce a rationale invariance loss as a simple regularization
technique, requiring only a few lines of code. Our experiments demonstrate that
the proposed approach achieves competitive results across various datasets,
despite its simplicity. Code is available at
\url{https://github.com/liangchen527/RIDG}.
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