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.

利用证据一致性预测方法（ECP）为图像分类器生成一致性预测集，通过基于非一致性评分函数，利用目标标签的对数几率值推导出的证据计算非一致性评分函数的组成部分：一致性预测中的不确定性启发式概念、不确定性惊喜度和期望效用，实验评估结果证明，ECP 在生成一致性预测集方面优于三种先进方法，同时保持了对真实标签的覆盖。