Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation

Deep Shankar Pandey; Hyomin Choi; Qi Yu

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arXiv · 2512.23753

Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation

Deep Shankar Pandey·Hyomin Choi·Qi Yu

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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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BibTeX RIS

Deep Shankar Pandey, Hyomin Choi, Qi Yu. 2025-12-27. Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation. https://arxiv.org/abs/2512.23753

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