ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability

• URL: http://arxiv.org/abs/2605.08858v1
• Date: Sat, 09 May 2026 10:14:46 GMT
• Title: ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability
• Abstract summary: We introduce ProDG: Generative Prototypes for Data-Free Post-Hoc Explainability.<n>It is a novel framework that leverages generative models to synthesize pure, high-fidelity prototypes directly from the frozen model's weights.<n>By establishing this new frontier in Data-Free XAI, ProDG unlocks robust visual interpretability for privacy-sensitive domains.
• Score: 9.86068604616604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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