Generalized Evidential Deep Learning: From a Bayesian Perspective
This work provides a principled theoretical understanding and a unified framework for EDL, benefiting researchers working on uncertainty estimation in deep learning.
The authors establish a theoretical foundation for Evidential Deep Learning (EDL) within a generalized Bayesian framework, proposing Generalized Evidential Deep Learning (GEDL) that unifies and extends existing variants. GEDL achieves comparable results to prior methods on classification, uncertainty estimation, and OOD detection tasks.
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.