Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation

• URL: http://arxiv.org/abs/2512.23753v1
• Date: Sat, 27 Dec 2025 11:26:18 GMT
• Title: Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation
• Authors: Deep Shankar Pandey, Hyomin Choi, Qi Yu,
• Abstract summary: Evidential deep learning models can quantify fine-grained uncertainty using learned evidence.<n>We develop a general family of activation functions and corresponding evidential regularizers to provide an alternative pathway for consistent evidence updates.
• Score: 20.241694857723218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evidential deep learning (EDL) models, based on Subjective Logic, introduce a principled and computationally efficient way to make deterministic neural networks uncertainty-aware. The resulting evidential models can quantify fine-grained uncertainty using learned evidence. However, the Subjective-Logic framework constrains evidence to be non-negative, requiring specific activation functions whose geometric properties can induce activation-dependent learning-freeze behavior: a regime where gradients become extremely small for samples mapped into low-evidence regions. We theoretically characterize this behavior and analyze how different evidential activations influence learning dynamics. Building on this analysis, we design a general family of activation functions and corresponding evidential regularizers that provide an alternative pathway for consistent evidence updates across activation regimes. Extensive experiments on four benchmark classification problems (MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet), two few-shot classification problems, and blind face restoration problem empirically validate the developed theory and demonstrate the effectiveness of the proposed generalized regularized evidential models.

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