Popular approaches for quantifying predictive uncertainty in deep neural networks often involve distributions over weights or multiple models, for instance via Markov Chain sampling, ensembling, or Monte Carlo dropout. These techniques usually incur overhead by having to train multiple model instances or do not produce very diverse predictions. This comprehensive and extensive survey aims to familiarize the reader with an alternative class of models based on the concept of Evidential Deep Learning: For unfamiliar data, they aim to admit "what they don't know", and fall back onto a prior belief. Furthermore, they allow uncertainty estimation in a single model and forward pass by parameterizing distributions over distributions. This survey recapitulates existing works, focusing on the implementation in a classification setting, before surveying the application of the same paradigm to regression. We also reflect on the strengths and weaknesses compared to other existing methods and provide the most fundamental derivations using a unified notation to aid future research.

翻译：量化深度神经网络预测置信度的常用方法通常涉及权重分布或多模型架构，例如通过马尔可夫链采样、集成学习或蒙特卡洛丢弃法实现。这些技术通常因需要训练多个模型实例而产生额外开销，或无法产生足够多样化的预测。本综述旨在全面系统地引导读者了解基于证据深度学习概念的替代模型类别：对于陌生数据，此类模型能够承认"未知信息"，并退回到先验信念。此外，通过参数化分布上的分布，它们能够在单模型单次前向传播中实现置信度估计。本综述首先回顾现有研究成果，聚焦分类场景下的实现方式，继而探讨同一范式在回归任务中的应用。我们还与其他现有方法进行优劣势对比，并使用统一符号体系提供最基础的推导过程，以助益未来研究。