Bayesian Deep Learning (BDL) gives access not only to aleatoric uncertainty, as standard neural networks already do, but also to epistemic uncertainty, a measure of confidence a model has in its own predictions. In this article, we show through experiments that the evolution of epistemic uncertainty metrics regarding the model size and the size of the training set, goes against theoretical expectations. More precisely, we observe that the epistemic uncertainty collapses literally in the presence of large models and sometimes also of little training data, while we expect the exact opposite behaviour. This phenomenon, which we call "epistemic uncertainty hole", is all the more problematic as it undermines the entire applicative potential of BDL, which is based precisely on the use of epistemic uncertainty. As an example, we evaluate the practical consequences of this uncertainty hole on one of the main applications of BDL, namely the detection of out-of-distribution samples

通过实验，我们观察到“认知不确定性孔洞”现象，即在大型模型和少量训练数据存在时，认知不确定性会明显降低，这与理论预期相反。该现象对基于认知不确定性的贝叶斯深度学习的实际应用产生问题，特别是在超出分布样本检测方面。

认知不确定性的漏洞：贝叶斯神经网络的问题