Evidential deep learning (EDL) has shown remarkable success in uncertainty estimation. However, there is still room for improvement, particularly in out-of-distribution (OOD) detection and classification tasks. The limited OOD detection performance of EDL arises from its inability to reflect the distance between the testing example and training data when quantifying uncertainty, while its limited classification performance stems from its parameterization of the concentration parameters. To address these limitations, we propose a novel method called Density Aware Evidential Deep Learning (DAEDL). DAEDL integrates the feature space density of the testing example with the output of EDL during the prediction stage, while using a novel parameterization that resolves the issues in the conventional parameterization. We prove that DAEDL enjoys a number of favorable theoretical properties. DAEDL demonstrates state-of-the-art performance across diverse downstream tasks related to uncertainty estimation and classification

翻译：证据深度学习（EDL）在不确定性估计方面已展现出显著的成功。然而，其仍有改进空间，特别是在分布外（OOD）检测和分类任务中。EDL在OOD检测方面的有限性能源于其在量化不确定性时无法反映测试样本与训练数据之间的距离，而其分类性能的局限性则源于其对浓度参数的参数化方式。为解决这些局限性，我们提出了一种名为密度感知证据深度学习（DAEDL）的新方法。DAEDL在预测阶段将测试样本的特征空间密度与EDL的输出相结合，同时采用一种新颖的参数化方式，解决了传统参数化存在的问题。我们证明了DAEDL具有一系列良好的理论性质。在与不确定性估计和分类相关的多种下游任务中，DAEDL均展示了最先进的性能。