In this paper, we propose Evidential Conformal Prediction (ECP) method for image classifiers to generate the conformal prediction sets. Our method is designed based on a non-conformity score function that has its roots in Evidential Deep Learning (EDL) as a method of quantifying model (epistemic) uncertainty in DNN classifiers. We use evidence that are derived from the logit values of target labels to compute the components of our non-conformity score function: the heuristic notion of uncertainty in CP, uncertainty surprisal, and expected utility. Our extensive experimental evaluation demonstrates that ECP outperforms three state-of-the-art methods for generating CP sets, in terms of their set sizes and adaptivity while maintaining the coverage of true labels.

翻译：本文提出了一种用于图像分类器的证据共形预测方法，以生成共形预测集合。该方法基于非共形性评分函数设计，该函数源于证据深度学习——一种用于量化深度神经网络分类器中模型认知不确定性的方法。我们利用从目标标签的logit值推导出的证据来计算非共形性评分函数的三个组成部分：共形预测中的启发式不确定性概念、不确定性惊奇度与期望效用。大量实验评估表明，在保持真实标签覆盖率的前提下，ECP方法在集合规模与自适应性方面均优于当前三种最先进的共形预测集生成方法。