With the wide proliferation of Deep Neural Networks in high-stake
applications, there is a growing demand for explainability behind their
decision-making process. Concept learning models attempt to learn high-level
'concepts' - abstract entities that align with human understanding, and thus
provide interpretability to DNN architectures. However, in this paper, we
demonstrate that present SOTA concept learning approaches suffer from two major
problems - lack of concept fidelity wherein the models fail to learn consistent
concepts among similar classes and limited concept interoperability wherein the
models fail to generalize learned concepts to new domains for the same task.
Keeping these in mind, we propose a novel self-explaining architecture for
concept learning across domains which - i) incorporates a new concept saliency
network for representative concept selection, ii) utilizes contrastive learning
to capture representative domain invariant concepts, and iii) uses a novel
prototype-based concept grounding regularization to improve concept alignment
across domains. We demonstrate the efficacy of our proposed approach over
current SOTA concept learning approaches on four widely used real-world
datasets. Empirical results show that our method improves both concept fidelity
measured through concept overlap and concept interoperability measured through
domain adaptation performance.

利用自我解释的架构跨领域进行概念学习，提出新的概念显著网络、对比学习和基于原型的概念基准规范化方法来解决概念一致性和泛化能力的问题，并在四个实际数据集上展示了方法的有效性。