Bayesian inference can quantify uncertainty in the predictions of neural networks using posterior distributions for model parameters and network output. By looking at these posterior distributions, one can separate the origin of uncertainty into aleatoric and epistemic contributions. One goal of uncertainty quantification is to inform on prediction accuracy. Here we show that prediction accuracy depends on both epistemic and aleatoric uncertainty in an intricate fashion that cannot be understood in terms of marginalized uncertainty distributions alone. How the accuracy relates to epistemic and aleatoric uncertainties depends not only on the model architecture, but also on the properties of the dataset. We discuss the significance of these results for active learning and introduce a novel acquisition function that outperforms common uncertainty-based methods. To arrive at our results, we approximated the posteriors using deep ensembles, for fully-connected, convolutional and attention-based neural networks.

翻译：贝叶斯推断可通过模型参数和网络输出的后验分布量化神经网络预测中的不确定性。通过审视这些后验分布，可将不确定性来源区分为偶然不确定性与认知不确定性。不确定性量化的目标之一是揭示预测准确性。本文证明，预测准确性以复杂方式同时依赖于认知不确定性，这种关联无法仅通过边缘化不确定性分布来理解。准确性与认知及偶然不确定性之间的依存关系不仅取决于模型架构，还与数据集特性密切相关。我们探讨了这些结果对主动学习的启示，并提出了一种新型采集函数，其性能优于基于不确定性的常见方法。为获得上述结论，我们采用深度集成方法逼近全连接网络、卷积网络及注意力机制网络的后验分布。