MaskDiME: 适应性遮蔽扩散用于精确且高效的视觉反事实解释
MaskDiME: Adaptive Masked Diffusion for Precise and Efficient Visual Counterfactual Explanations
AI总结：
本文提出MaskDiME，一种高效的扩散框架，通过局部采样实现语义一致性和空间精度，提升视觉反事实解释的效率和准确性。
摘要：
视觉反事实解释旨在揭示最小的语义修改以改变模型预测，提供深度神经网络的因果和可解释见解。然而，现有基于扩散的反事实生成方法往往计算成本高、采样慢且在局部化修改区域不精确。为解决这些限制，我们提出MaskDiME，一种简单、快速且有效的扩散框架，通过局部采样统一语义一致性和空间精度。我们的方法适应性地聚焦于决策相关区域，实现局部化且语义一致的反事实生成，同时保持高图像保真度。我们的无训练框架MaskDiME在基准数据集上表现可比或达到最先进的性能，比基线快30倍，建立了高效反事实解释的实用且可推广的解决方案。
英文摘要：
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, yet 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, performs inference over 30x faster than the baseline 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
