Beyond Patches: Mining Interpretable Part-Prototypes for Explainable AI

• URL: http://arxiv.org/abs/2504.12197v1
• Date: Wed, 16 Apr 2025 15:48:21 GMT
• Title: Beyond Patches: Mining Interpretable Part-Prototypes for Explainable AI
• Authors: Mahdi Alehdaghi, Rajarshi Bhattacharya, Pourya Shamsolmoali, Rafael M. O. Cruz, Maguelonne Heritier, Eric Granger,
• Abstract summary: Part-prototypical concept mining network (PCMNet) is proposed to learn interpretable prototypes from meaningful regions.<n>PCMNet clusters prototypes into concept groups, creating semantically grounded explanations without requiring additional annotations.<n>Our experiments show that PCMNet can provide a high level of interpretability, stability, and robustness under clean and occluded scenarios.
• Score: 10.687381287384524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning has provided considerable advancements for multimedia systems, yet the interpretability of deep models remains a challenge. State-of-the-art post-hoc explainability methods, such as GradCAM, provide visual interpretation based on heatmaps but lack conceptual clarity. Prototype-based approaches, like ProtoPNet and PIPNet, offer a more structured explanation but rely on fixed patches, limiting their robustness and semantic consistency. To address these limitations, a part-prototypical concept mining network (PCMNet) is proposed that dynamically learns interpretable prototypes from meaningful regions. PCMNet clusters prototypes into concept groups, creating semantically grounded explanations without requiring additional annotations. Through a joint process of unsupervised part discovery and concept activation vector extraction, PCMNet effectively captures discriminative concepts and makes interpretable classification decisions. Our extensive experiments comparing PCMNet against state-of-the-art methods on multiple datasets show that it can provide a high level of interpretability, stability, and robustness under clean and occluded scenarios.

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