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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.23870 |
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| _version_ | 1866910940327313408 |
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| author | Key, Hojer |
| author_facet | Key, Hojer |
| contents | This paper introduces a novel formal SAT-based explanation model for deep learning in video understanding. The proposed method integrates SAT solving techniques with the principles of formal explainable AI to address the limitations of existing XAI techniques in this domain. By encoding deep learning models and video data into a logical framework and formulating explanation queries as satisfiability problems, the method aims to generate logic-based explanations with formal guarantees. The paper details the conceptual framework, the process of encoding deep learning models and video data, the formulation of "Why?" and "Why not?" questions, and a novel architecture integrating a SAT solver with a deep learning video understanding model. While challenges related to computational complexity and the representational power of propositional logic remain, the proposed approach offers a promising direction for enhancing the explainability of deep learning in the complex and critical domain of video understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23870 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A SAT-centered XAI method for Deep Learning based Video Understanding Key, Hojer Logic in Computer Science This paper introduces a novel formal SAT-based explanation model for deep learning in video understanding. The proposed method integrates SAT solving techniques with the principles of formal explainable AI to address the limitations of existing XAI techniques in this domain. By encoding deep learning models and video data into a logical framework and formulating explanation queries as satisfiability problems, the method aims to generate logic-based explanations with formal guarantees. The paper details the conceptual framework, the process of encoding deep learning models and video data, the formulation of "Why?" and "Why not?" questions, and a novel architecture integrating a SAT solver with a deep learning video understanding model. While challenges related to computational complexity and the representational power of propositional logic remain, the proposed approach offers a promising direction for enhancing the explainability of deep learning in the complex and critical domain of video understanding. |
| title | A SAT-centered XAI method for Deep Learning based Video Understanding |
| topic | Logic in Computer Science |
| url | https://arxiv.org/abs/2503.23870 |