Uncertainty estimation of the 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 the 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 the TTMA class-dependent uncertainty (TTMA-CDU), 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-CDU provides information on class confusion and class similarity for both datasets.

翻译：对经过培训的深层学习网络的不确定性估计对于优化学习效率和评价网络预测的可靠性是有价值的。在本文件中,我们提出一种方法,用测试-时间混杂增强(TTMA)来估计深层学习图像分类的不确定性。为了提高在现有的悬浮性不确定性中区分正确和不正确的预测的能力,我们采用了TTMA数据不确定性(TTMA-DU),对测试数据和测量预测标签直方图的梯子。除了TTMA-DU外,我们还提议TTMA级依赖级的不确定性(TTMA-CDU),该不确定性可捕捉到各个班子特有的疏漏性不确定性,并洞察到经过培训的网络中的阶级混乱和类类相似性。我们验证了关于ISIC-18皮肤损伤诊断数据集和CIFAR-100真实世界图像分类数据集的拟议方法。我们的实验显示:(1)TTMA-DU能够更有效地区分正确和不正确的预测,而与由于混混化造成的现有不确定性措施相比,TTMA-CDU提供两类关于分类混乱和类相似性的信息。