Visualizing Uncertainty: Spatial Maps of Missing and Conflicting Evidence in Deep Learning

2026-06-14

AI Summary

This work addresses the limitation of existing deep learning approaches to uncertainty quantification, which struggle to distinguish between uncertainty arising from missing evidence (vacuity) and that stemming from conflicting evidence (dissonance), while also lacking spatial interpretability. To overcome this, the paper introduces the Uncertainty Activation Map (UAM) framework, which uniquely integrates the concepts of vacuity and dissonance from subjective logic with Full Gradient-based class activation mapping (FullGrad). By leveraging evidential deep learning, UAM generates spatially resolved visualizations of uncertainty that are both theoretically grounded and intuitively interpretable. The method effectively localizes the spatial origins of different uncertainty types across multiple benchmark datasets, offering an explainable visual feedback mechanism for assessing model reliability in complex vision tasks.

Abstract

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.

Problem

• uncertainty visualization
• spatial uncertainty
• evidential deep learning
• model interpretability
• deep learning reliability

Innovation

• Uncertainty Activation Map
• Evidential Deep Learning
• Full-Gradient CAM
• Vacuity and Dissonance
• Uncertainty Visualization