Uncertainty estimation of trained deep learning networks is valuable for optimizing learning efficiency and evaluating the reliability of network predictions. In this paper, we propose a method for estimating uncertainty in deep learning image classification using test-time mixup augmentation (TTMA). To improve the ability to distinguish correct and incorrect predictions in existing aleatoric uncertainty, we introduce TTMA data uncertainty (TTMA-DU) by applying mixup augmentation to test data and measuring the entropy of the predicted label histogram. In addition to TTMA-DU, we propose TTMA class-specific uncertainty (TTMA-CSU), which captures aleatoric uncertainty specific to individual classes and provides insight into class confusion and class similarity within the trained network. We validate our proposed methods on the ISIC-18 skin lesion diagnosis dataset and the CIFAR-100 real-world image classification dataset. Our experiments show that (1) TTMA-DU more effectively differentiates correct and incorrect predictions compared to existing uncertainty measures due to mixup perturbation, and (2) TTMA-CSU provides information on class confusion and class similarity for both datasets.

翻译：训练后的深度学习网络的不确定性估计对于优化学习效率和评估网络预测可靠性具有重要价值。本文提出一种基于测试时混合增强（TTMA）的深度学习图像分类不确定性估计方法。为提升现有随机不确定性中区分正确与错误预测的能力，我们通过将混合增强应用于测试数据并测量预测标签直方图的熵，引入TTMA数据不确定性（TTMA-DU）。除TTMA-DU外，我们还提出TTMA类别特定不确定性（TTMA-CSU），该方法可捕捉特定于单个类别的随机不确定性，并揭示训练网络中类别混淆与类别相似性的深层特征。我们在ISIC-18皮肤病变诊断数据集和CIFAR-100真实世界图像分类数据集上验证了所提方法。实验结果表明：（1）得益于混合扰动机制，TTMA-DU比现有不确定性度量方法能更有效地区分正确与错误预测；（2）TTMA-CSU在两个数据集上均能提供关于类别混淆与类别相似性的有效信息。