ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability
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arXiv
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| Format: | Preprint |
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2026
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| author | Borycki, Piotr Trędowicz, Magdalena Tabor, Jacek Struski, Łukasz Spurek, Przemysław |
| author_facet | Borycki, Piotr Trędowicz, Magdalena Tabor, Jacek Struski, Łukasz Spurek, Przemysław |
| contents | Ante-hoc interpretability methods based on prototypes provide highly accurate explanations by utilizing the intuitive "this looks like that" reasoning paradigm. On the other hand, post-hoc models can explain predictions for a single image without relying on an underlying dataset or requiring costly neural network retraining. Recent approaches successfully solve the retraining problem for prototype-based networks. However, they still face a fundamental limitation: they require access to a subset of data (e.g., a test or validation set) to search for and extract the visual prototypes. In this paper, we address this issue and introduce ProDG: Generative Prototypes for Data-Free Post-Hoc Explainability, a novel framework that leverages generative models to synthesize pure, high-fidelity prototypes directly from the frozen model's weights, completely eliminating the dependency on any external data. By establishing this new frontier in Data-Free XAI, ProDG unlocks robust visual interpretability for privacy-sensitive domains, where original data is strictly restricted or fundamentally inaccessible. Project page: https://github.com/piotr310100/ProDG |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08858 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability Borycki, Piotr Trędowicz, Magdalena Tabor, Jacek Struski, Łukasz Spurek, Przemysław Computer Vision and Pattern Recognition Ante-hoc interpretability methods based on prototypes provide highly accurate explanations by utilizing the intuitive "this looks like that" reasoning paradigm. On the other hand, post-hoc models can explain predictions for a single image without relying on an underlying dataset or requiring costly neural network retraining. Recent approaches successfully solve the retraining problem for prototype-based networks. However, they still face a fundamental limitation: they require access to a subset of data (e.g., a test or validation set) to search for and extract the visual prototypes. In this paper, we address this issue and introduce ProDG: Generative Prototypes for Data-Free Post-Hoc Explainability, a novel framework that leverages generative models to synthesize pure, high-fidelity prototypes directly from the frozen model's weights, completely eliminating the dependency on any external data. By establishing this new frontier in Data-Free XAI, ProDG unlocks robust visual interpretability for privacy-sensitive domains, where original data is strictly restricted or fundamentally inaccessible. Project page: https://github.com/piotr310100/ProDG |
| title | ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.08858 |