这篇论文把不确定性可视化了，能告诉你模型哪里没学过、哪里证据打架，比光给一个置信度有用多了。
论文提出不确定性激活图（UAM）框架，结合证据深度学习（EDL）和FullGrad，生成空间不确定性热力图。该方法区分空缺（缺乏证据）和不和谐（证据冲突）两种不确定性类型。通过主观逻辑和完全梯度分解，UAM可以定位图像中导致模型不确定的区域。在多个基准数据集上的评估表明，该框架弥补了不确定性量化与可解释性之间的差距。
Visualizing Uncertainty: Spatial Maps of Missing and Conflicting Evidence in Deep Learning
Understanding when and why deep neural networks are uncertain is crucial for deploying reliable machine learning systems in safety-critical domains. While existing uncertainty quantification methods provide scalar measures of model confidence, they offer limited insight into which spatial regions of an input contribute to different types of uncertainty. We propose a novel visualization framework, Uncertainty Activation Map (UAM), that combines Evidential Deep Learning (EDL) with Full-Gradient Class Activation Mapping (FullGrad) to generate interpretable spatial uncertainty activation maps. Our approach distinguishes between two fundamental types of uncertainty: vacuity, representing lack of evidence, and dissonance, capturing conflicting evidence between competing hypotheses. By leveraging the complete gradient decomposition property of FullGrad and the principled uncertainty quantification of Subjective Logic, our method produces theoretically grounded visualizations that highlight specific image regions responsible for model uncertainty. With this framework, vacuity and dissonance activation maps are generated by computing belief-weighted attributions, enabling identification of where models lack knowledge versus where they encounter ambiguous evidence. Extensive evaluations across multiple benchmark datasets demonstrate that the proposed framework effectively addresses the critical gap between uncertainty quantification and explainability, providing intuitive visual feedback to assess model reliability in complex visual recognition tasks.