Domain Generalization via Rationale Invariance

Liang Chen, Yong Zhang, Yibing Song, Anton van den Hengel, Lingqiao Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 1751-1760


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

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@InProceedings{Chen_2023_ICCV, author = {Chen, Liang and Zhang, Yong and Song, Yibing and van den Hengel, Anton and Liu, Lingqiao}, title = {Domain Generalization via Rationale Invariance}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {1751-1760} }