MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes

• URL: http://arxiv.org/abs/2404.08968v3
• Date: Tue, 23 Apr 2024 07:13:30 GMT
• Title: MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes
• Authors: Bor-Shiun Wang, Chien-Yi Wang, Wei-Chen Chiu,
• Abstract summary: We argue that explanations lacking insights into the decision processes of low and mid-level features are neither fully faithful nor useful. We propose a novel paradigm that learns and aligns multi-level concept prototype distributions for classification purposes via Class-aware Concept Distribution (CCD) loss.
• Score: 24.28807025839685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in post-hoc and inherently interpretable methods have markedly enhanced the explanations of black box classifier models. These methods operate either through post-analysis or by integrating concept learning during model training. Although being effective in bridging the semantic gap between a model's latent space and human interpretation, these explanation methods only partially reveal the model's decision-making process. The outcome is typically limited to high-level semantics derived from the last feature map. We argue that the explanations lacking insights into the decision processes at low and mid-level features are neither fully faithful nor useful. Addressing this gap, we introduce the Multi-Level Concept Prototypes Classifier (MCPNet), an inherently interpretable model. MCPNet autonomously learns meaningful concept prototypes across multiple feature map levels using Centered Kernel Alignment (CKA) loss and an energy-based weighted PCA mechanism, and it does so without reliance on predefined concept labels. Further, we propose a novel classifier paradigm that learns and aligns multi-level concept prototype distributions for classification purposes via Class-aware Concept Distribution (CCD) loss. Our experiments reveal that our proposed MCPNet while being adaptable to various model architectures, offers comprehensive multi-level explanations while maintaining classification accuracy. Additionally, its concept distribution-based classification approach shows improved generalization capabilities in few-shot classification scenarios.

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