SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918104344297472 |
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| author | Dubovik, Viktar Struski, Łukasz Tabor, Jacek Rymarczyk, Dawid |
| author_facet | Dubovik, Viktar Struski, Łukasz Tabor, Jacek Rymarczyk, Dawid |
| contents | Understanding the decisions made by deep neural networks is essential in high-stakes domains such as medical imaging and autonomous driving. Yet, these models often lack transparency, particularly in computer vision. Prototypical-parts-based neural networks have emerged as a promising solution by offering concept-level explanations. However, most are limited to fine-grained classification tasks, with few exceptions such as InfoDisent. InfoDisent extends prototypical models to large-scale datasets like ImageNet, but produces complex explanations.
We introduce Sparse Information Disentanglement for Explainability (SIDE), a novel method that improves the interpretability of prototypical parts through a dedicated training and pruning scheme that enforces sparsity. Combined with sigmoid activations in place of softmax, this approach allows SIDE to associate each class with only a small set of relevant prototypes. Extensive experiments show that SIDE matches the accuracy of existing methods while reducing explanation size by over $90\%$, substantially enhancing the understandability of prototype-based explanations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_19321 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence Dubovik, Viktar Struski, Łukasz Tabor, Jacek Rymarczyk, Dawid Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Understanding the decisions made by deep neural networks is essential in high-stakes domains such as medical imaging and autonomous driving. Yet, these models often lack transparency, particularly in computer vision. Prototypical-parts-based neural networks have emerged as a promising solution by offering concept-level explanations. However, most are limited to fine-grained classification tasks, with few exceptions such as InfoDisent. InfoDisent extends prototypical models to large-scale datasets like ImageNet, but produces complex explanations. We introduce Sparse Information Disentanglement for Explainability (SIDE), a novel method that improves the interpretability of prototypical parts through a dedicated training and pruning scheme that enforces sparsity. Combined with sigmoid activations in place of softmax, this approach allows SIDE to associate each class with only a small set of relevant prototypes. Extensive experiments show that SIDE matches the accuracy of existing methods while reducing explanation size by over $90\%$, substantially enhancing the understandability of prototype-based explanations. |
| title | SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.19321 |