MaskDiME: Adaptive Masked Diffusion for Precise and Efficient Visual Counterfactual Explanations

• URL: http://arxiv.org/abs/2602.18792v1
• Date: Sat, 21 Feb 2026 10:53:50 GMT
• Title: MaskDiME: Adaptive Masked Diffusion for Precise and Efficient Visual Counterfactual Explanations
• Authors: Changlu Guo, Anders Nymark Christensen, Anders Bjorholm Dahl, Morten Rieger Hannemose,
• Abstract summary: MaskDiME is a diffusion-based counterfactual generation framework.<n>It unifies semantic consistency and spatial precision through localized sampling.<n>It achieves over 30x faster inference than the baseline method.
• Score: 7.325562054466611
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual counterfactual explanations aim to reveal the minimal semantic modifications that can alter a model's prediction, providing causal and interpretable insights into deep neural networks. However, existing diffusion-based counterfactual generation methods are often computationally expensive, slow to sample, and imprecise in localizing the modified regions. To address these limitations, we propose MaskDiME, a simple, fast, and effective diffusion framework that unifies semantic consistency and spatial precision through localized sampling. Our approach adaptively focuses on decision-relevant regions to achieve localized and semantically consistent counterfactual generation while preserving high image fidelity. Our training-free framework, MaskDiME, achieves over 30x faster inference than the baseline method and achieves comparable or state-of-the-art performance across five benchmark datasets spanning diverse visual domains, establishing a practical and generalizable solution for efficient counterfactual explanation.

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