Generalized Evidential Deep Learning: From a Bayesian Perspective

2026-05-25

AI Summary

This work addresses the lack of a unified theoretical framework in existing evidential deep learning (EDL) approaches, which obscures their intrinsic connections and design principles. By casting EDL within a generalized Bayesian perspective for the first time, this study elucidates the fundamental nature of distributional uncertainty and systematically dissects the interplay among prior specification, posterior updating, and training objectives. Building on this insight, the authors propose a unified and extensible Generalized Evidential Deep Learning (GEDL) framework. Through component-wise decoupling, GEDL not only integrates and generalizes existing methods but also achieves state-of-the-art performance in classification, uncertainty quantification, and out-of-distribution detection, all while enjoying a rigorous theoretical foundation.

Abstract

Evidential Deep Learning (EDL) has emerged as an efficient, sampling-free strategy for uncertainty estimation. A series of EDL variants have been proposed to address specific limitations of the original framework, achieving notable success. However, the underlying theoretical structure of EDL and the relationships among these variants have received limited systematic investigation. In this work, we establish a principled theoretical foundation for EDL by interpreting it within a generalized Bayesian framework that includes prior specification, posterior update, and training objective. We further characterize evidential uncertainty from a Bayesian distributional uncertainty viewpoint, established via asymptotic analysis. Building on this perspective, we further propose Generalized Evidential Deep Learning (GEDL), a unified and extensible framework that explicitly disentangles the roles of individual components and systematically relates GEDL to existing variants. Extensive experiments demonstrate that GEDL yields comparable results on classification, uncertainty estimation and OOD detections, with theoretical grounding.

Problem

• Evidential Deep Learning
• Bayesian framework
• uncertainty estimation
• theoretical foundation
• distributional uncertainty

Innovation

• Generalized Evidential Deep Learning
• Bayesian framework
• uncertainty estimation
• distributional uncertainty
• evidential deep learning