Understanding the trustworthiness of a prediction yielded by a classifier is
critical for the safe and effective use of AI models. Prior efforts have been
proven to be reliable on small-scale datasets. In this work, we study the
problem of predicting trustworthiness on real-world large-scale
本文提出了一种基于 Correctness Ranking Loss 的深度神经网络训练方法，可以对类别概率进行显式的正序排名，提高置信度预测。该方法易于实现，不需要额外的计算代价，适用于现有体系结构，并且在分类基准数据集上表现良好。同时，还对置信度估计相关的任务，如超出分布检测和主动学习，具有相同的可靠性。