Enhancing Bottleneck Concept Learning in Image Classification

Xingfu Cheng, Zhaofeng Niu, Zhouqiang Jiang, Liangzhi Li

Abstract:

Deep neural networks (DNNs) have demonstrated exceptional performance
in image classification. However, their “black-box” nature raises concerns about trust
and transparency, particularly in high-stakes fields such as healthcare and autonomous
systems. While explainable AI (XAI) methods attempt to address these concerns through
feature- or concept-based explanations, existing approaches are often limited by the need
for manually defined concepts, overly abstract granularity, or misalignment with human
semantics. This paper introduces the Enhanced Bottleneck Concept Learner (E-BotCL),
a self-supervised framework that autonomously discovers task-relevant, interpretable
semantic concepts via a dual-path contrastive learning strategy and multi-task regularization. By combining contrastive learning to build robust concept prototypes, attention
mechanisms for spatial localization, and feature aggregation to activate concepts, E-BotCL
enables end-to-end concept learning and classification without requiring human supervision. Experiments conducted on the CUB200 and ImageNet datasets demonstrated that
E-BotCL significantly enhanced interpretability while maintaining classification accuracy.
Specifically, two interpretability metrics, the Concept Discovery Rate (CDR) and Concept
Consistency (CC), improved by 0.6104 and 0.4486, respectively. This work advances the
balance between model performance and transparency, offering a scalable solution for
interpretable decision-making in complex vision tasks.