Deep Neural Networks (DNNs) do not inherently compute or exhibit empirically-justified task confidence. In mission critical applications, it is important to both understand associated DNN reasoning and its supporting evidence. In this paper, we propose a novel Bayesian approach to extract explanations, justifications, and uncertainty estimates from DNNs. Our approach is efficient both in terms of memory and computation, and can be applied to any black box DNN without any retraining, including applications to anomaly detection and out-of-distribution detection tasks. We validate our approach on the CIFAR-10 dataset, and show that it can significantly improve the interpretability and reliability of DNNs.

翻译：深度神经网络（DNNs）本身既不计算也不展现基于经验的任务置信度。在关键任务应用中，理解相关DNN推理及其支撑证据至关重要。本文提出一种新的贝叶斯方法，用于从DNN中提取解释、论证和不确定性估计。该方法在内存和计算方面均具有高效性，可应用于任意黑箱DNN而无需重新训练，包括在异常检测和分布外检测任务中的应用。我们在CIFAR-10数据集上验证了该方法，结果表明其能显著提升DNN的可解释性和可靠性。