Neural networks have revolutionized the field of machine learning with increased predictive capability. In addition to improving the predictions of neural networks, there is a simultaneous demand for reliable uncertainty quantification on estimates made by machine learning methods such as neural networks. Bayesian neural networks (BNNs) are an important type of neural network with built-in capability for quantifying uncertainty. This paper discusses aleatoric and epistemic uncertainty in BNNs and how they can be calculated. With an example dataset of images where the goal is to identify the amplitude of an event in the image, it is shown that epistemic uncertainty tends to be lower in images which are well-represented in the training dataset and tends to be high in images which are not well-represented. An algorithm for out-of-distribution (OoD) detection with BNN epistemic uncertainty is introduced along with various experiments demonstrating factors influencing the OoD detection capability in a BNN. The OoD detection capability with epistemic uncertainty is shown to be comparable to the OoD detection in the discriminator network of a generative adversarial network (GAN) with comparable network architecture.

翻译：神经网络凭借其强大的预测能力彻底改变了机器学习领域。在提升神经网络预测性能的同时，对神经网络等机器学习方法所做出估计进行可靠的不确定性量化也成为了迫切需求。贝叶斯神经网络（BNNs）是一类具有内置不确定性量化能力的重要神经网络。本文探讨了BNN中的偶然不确定性和认知不确定性及其计算方法。通过一个以识别图像中事件振幅为目标的标准图像数据集，研究表明：在训练数据集中具有充分代表性的图像中，认知不确定性通常较低；而在代表性不足的图像中，认知不确定性往往较高。本文提出了基于BNN认知不确定性的分布外（OoD）检测算法，并通过多项实验展示了影响BNN中OoD检测能力的因素。实验证明，基于认知不确定性的OoD检测能力与具有相似网络架构的生成对抗网络（GAN）判别器网络的OoD检测性能相当。