Uncertainty-quantification methods are applied to estimate the confidence of deep-neural-networks classifiers over their predictions. However, most widely used methods are known to be overconfident. We address this problem by developing an algorithm that exploits the latent-space representation of data points fed into the network, to assess the accuracy of their prediction. Using the latent-space representation generated by the fraction of training set that the network classifies correctly, we build a statistical model that is able to capture the likelihood of a given prediction. We show on a synthetic dataset that commonly used methods are mostly overconfident. Overconfidence occurs also for predictions made on data points that are outside the distribution that generated the training data. In contrast, our method can detect such out-of-distribution data points as inaccurately predicted, thus aiding in the automatic detection of outliers.

翻译：不确定性量化方法被用于估计深度神经网络分类器对其预测结果的置信度。然而，大多数广泛使用的方法已知存在过度自信的问题。我们通过开发一种算法来解决这一问题，该算法利用输入网络的数据点在潜在空间中的表征来评估其预测的准确性。利用网络正确分类的训练集部分所生成的潜在空间表征，我们构建了一个能够捕捉给定预测可能性的统计模型。我们在合成数据集上证明，常用方法大多存在过度自信的问题。这种过度自信同样发生在对训练数据分布之外的数据点进行预测时。相比之下，我们的方法能够检测出此类分布外数据点具有不准确的预测，从而有助于自动检测异常值。