Generating Part-Based Global Explanations Via Correspondence

Kunal Rathore; Prasad Tadepalli

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arXiv · 2509.15393

Generating Part-Based Global Explanations Via Correspondence

Kunal Rathore·Prasad Tadepalli

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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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Kunal Rathore, Prasad Tadepalli. 2025-09-18. Generating Part-Based Global Explanations Via Correspondence. https://arxiv.org/abs/2509.15393

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