Generating Part-Based Global Explanations Via Correspondence
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918144148242432 |
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| author | Rathore, Kunal Tadepalli, Prasad |
| author_facet | Rathore, Kunal Tadepalli, Prasad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generating Part-Based Global Explanations Via Correspondence Rathore, Kunal Tadepalli, Prasad Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Generating Part-Based Global Explanations Via Correspondence |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.15393 |