Concept Bottleneck Models (CBMs) enhance interpretability by explaining predictions through human-understandable concepts but typically assume that training and test data share the same distribution. This assumption often fails under domain shifts, leading to degraded performance and poor generalization. To address these limitations and improve the robustness of CBMs, we propose the Concept-based Unsupervised Domain Adaptation (CUDA) framework. CUDA is designed to: (1) align concept representations across domains using adversarial training, (2) introduce a relaxation threshold to allow minor domain-specific differences in concept distributions, thereby preventing performance drop due to over-constraints of these distributions, (3) infer concepts directly in the target domain without requiring labeled concept data, enabling CBMs to adapt to diverse domains, and (4) integrate concept learning into conventional domain adaptation (DA) with theoretical guarantees, improving interpretability and establishing new benchmarks for DA. Experiments demonstrate that our approach significantly outperforms the state-of-the-art CBM and DA methods on real-world datasets.

本研究解决了概念瓶颈模型在域转移下性能下降的问题，提出了基于概念的无监督领域适应（CUDA）框架。CUDA通过对抗训练对齐跨域概念表示，并引入松弛阈值来允许小的领域特定差异，从而增强了模型的鲁棒性，并实现了在目标领域中直接推断概念。实验结果表明，该方法在真实数据集上显著优于现有的最佳概念瓶颈模型和领域适应方法。