Generating Part-Based Global Explanations Via Correspondence

• URL: http://arxiv.org/abs/2509.15393v1
• Date: Thu, 18 Sep 2025 20:00:49 GMT
• Title: Generating Part-Based Global Explanations Via Correspondence
• Authors: Kunal Rathore, Prasad Tadepalli,
• Abstract summary: We propose an approach that leverages user-defined part labels from a limited set of images and efficiently transfers them to a larger dataset.<n>This enables the generation of global symbolic explanations by aggregating part-based local explanations, ultimately providing human-understandable explanations for model decisions on a large scale.
• Score: 8.83354835766461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models are notoriously opaque. Existing explanation methods often focus on localized visual explanations for individual images. Concept-based explanations, while offering global insights, require extensive annotations, incurring significant labeling cost. We propose an approach that leverages user-defined part labels from a limited set of images and efficiently transfers them to a larger dataset. This enables the generation of global symbolic explanations by aggregating part-based local explanations, ultimately providing human-understandable explanations for model decisions on a large scale.

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