This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL). EDL is one example of a class of uncertainty-aware deep learning approaches designed to provide confidence (or epistemic uncertainty) about the current test sample. In particular for computer vision and bidirectional encoder large language models, the `evidential signal' arising from the Dirichlet strength in EDL can, in some cases, discriminate between classes, which is particularly strong when using large language models. We hypothesise that the KL regularisation term causes EDL to couple aleatoric and epistemic uncertainty. In this paper, we empirically investigate the correlations between misclassification and evaluated uncertainty, and show that EDL's `evidential signal' is due to misclassification bias. We critically evaluate EDL with other Dirichlet-based approaches, namely Generative Evidential Neural Networks (EDL-GEN) and Prior Networks, and show theoretically and empirically the differences between these loss functions. We conclude that EDL's coupling of uncertainty arises from these differences due to the use (or lack) of out-of-distribution samples during training.

翻译：本文揭示了证据深度学习（EDL）中由不确定性值产生的一个证据信号。EDL是一类不确定性感知深度学习方法中的一个实例，旨在为当前测试样本提供置信度（或认知不确定性）。特别是在计算机视觉和双向编码器大型语言模型中，由EDL中狄利克雷强度产生的“证据信号”在某些情况下能够区分不同类别，而在使用大型语言模型时这一表现尤为显著。我们假设KL正则化项导致EDL将偶然不确定性和认知不确定性耦合在一起。本文通过实验研究了误分类与评估不确定性之间的相关性，并表明EDL的“证据信号”源自误分类偏差。我们还将EDL与其他基于狄利克雷的方法（即生成式证据神经网络（EDL-GEN）和先验网络）进行了批判性评估，从理论和实验角度揭示了这些损失函数之间的差异。我们得出结论：由于训练过程中是否使用分布外样本，这些差异导致了EDL的不确定性耦合现象。