Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental requirement in high-stakes applications. Unfortunately, modern deep neural networks are often overconfident for their erroneous predictions. In this work, we exploit the easily available outlier samples, i.e., unlabeled samples coming from non-target classes, for helping detect misclassification errors. Particularly, we find that the well-known Outlier Exposure, which is powerful in detecting out-of-distribution (OOD) samples from unknown classes, does not provide any gain in identifying misclassification errors. Based on these observations, we propose a novel method called OpenMix, which incorporates open-world knowledge by learning to reject uncertain pseudo-samples generated via outlier transformation. OpenMix significantly improves confidence reliability under various scenarios, establishing a strong and unified framework for detecting both misclassified samples from known classes and OOD samples from unknown classes. The code is publicly available at https://github.com/Impression2805/OpenMix.

翻译：深度神经网络分类器的可靠置信度估计是高风险应用中的一个关键且基础性的挑战。然而，现代深度神经网络常对其错误预测表现出过度自信。本研究利用易于获取的异常样本（即来自非目标类别的无标签样本）辅助检测误分类错误。具体而言，我们发现常用于检测未知类别分布外样本的经典方法Outlier Exposure（异常暴露）在识别误分类错误上并无增益。基于此观察，我们提出了一种名为OpenMix的新方法，通过学习拒绝经异常变换生成的可疑伪样本，融合开放世界知识。OpenMix在多种场景下显著提升了置信度可靠性，为检测已知类别的误分类样本及未知类别的分布外样本建立了强健统一的框架。相关代码已开源至https://github.com/Impression2805/OpenMix。