DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models

• URL: http://arxiv.org/abs/2510.14741v1
• Date: Thu, 16 Oct 2025 14:43:25 GMT
• Title: DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models
• Authors: Simone Carnemolla, Matteo Pennisi, Sarinda Samarasinghe, Giovanni Bellitto, Simone Palazzo, Daniela Giordano, Mubarak Shah, Concetto Spampinato,
• Abstract summary: DEXTER is a data-free framework that generates global, textual explanations of visual classifiers.<n>We show that DEXTER produces accurate, interpretable outputs.<n> Experiments on ImageNet, Waterbirds, CelebA, and FairFaces confirm that DEXTER outperforms existing approaches in global model explanation and class-level bias reporting.
• Score: 49.25757423776323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework that employs diffusion models and large language models to generate global, textual explanations of visual classifiers. DEXTER operates by optimizing text prompts to synthesize class-conditional images that strongly activate a target classifier. These synthetic samples are then used to elicit detailed natural language reports that describe class-specific decision patterns and biases. Unlike prior work, DEXTER enables natural language explanation about a classifier's decision process without access to training data or ground-truth labels. We demonstrate DEXTER's flexibility across three tasks-activation maximization, slice discovery and debiasing, and bias explanation-each illustrating its ability to uncover the internal mechanisms of visual classifiers. Quantitative and qualitative evaluations, including a user study, show that DEXTER produces accurate, interpretable outputs. Experiments on ImageNet, Waterbirds, CelebA, and FairFaces confirm that DEXTER outperforms existing approaches in global model explanation and class-level bias reporting. Code is available at https://github.com/perceivelab/dexter.

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