Domain Generalization via Rationale Invariance
Liang Chen Yong Zhang Yibing Song Anton van den Hengel Lingqiao Liu
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}.