MaskDiME：面向精确高效视觉反事实解释的自适应掩码扩散
MaskDiME: Adaptive Masked Diffusion for Precise and Efficient Visual Counterfactual Explanations
把扩散采样限制在决策相关区域、以掩码引导的局部采样实现免训练反事实生成，为视觉研究者提供一种可复用的思路：用扩散先验做模型行为归因，且推理开销比基线低一个数量级以上。
原文依据 Our training-free framework, MaskDiME, performs inference over 30x faster than the baseline
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉反事实解释旨在揭示能够改变模型预测的最小语义修改，从而为深度神经网络提供因果性、可解释的洞察。然而，现有的基于扩散的反事实生成方法往往计算开销大、采样速度慢，且在定位被修改区域时不够精确。为解决这些局限，我们提出 MaskDiME，一个简单、快速且有效的扩散框架，通过局部采样将语义一致性与空间精确性统一起来。我们的方法自适应地聚焦于与决策相关的区域，在保持高图像保真度的同时实现局部化且语义一致的反事实生成。我们的免训练框架 MaskDiME 的推理速度比基线快 30 倍以上，并在涵盖多样视觉领域的五个基准数据集上取得可比或最优的性能，为高效反事实解释提供了一个实用且可泛化的解决方案。我们的代码已公开于 https://github.com/clguo/MaskDiME。
英文原文摘要 1087 字符
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. Our code is available at https://github.com/clguo/MaskDiME.