Safe deployment of deep neural networks in high-stake real-world applications requires theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled framework for uncertainty quantification of deep models in the form of prediction set for classification tasks with a user-specified coverage (i.e., true class label is contained with high probability). This paper proposes a novel algorithm referred to as Neighborhood Conformal Prediction (NCP) to improve the efficiency of uncertainty quantification from CP for deep classifiers (i.e., reduce prediction set size). The key idea behind NCP is to use the learned representation of the neural network to identify k nearest-neighbors calibration examples for a given testing input and assign them importance weights proportional to their distance to create adaptive prediction sets. We theoretically show that if the learned data representation of the neural network satisfies some mild conditions, NCP will produce smaller prediction sets than traditional CP algorithms. Our comprehensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using diverse deep neural networks strongly demonstrate that NCP leads to significant reduction in prediction set size over prior CP methods.

翻译：在高风险现实应用中安全部署深度神经网络需要理论上可靠的不确定性量化。共形预测（CP）是一种基于原则的框架，用于深度模型的不确定性量化，其形式为分类任务的预测集，具有用户指定的覆盖度（即真实类别标签以高概率被包含）。本文提出一种名为邻域共形预测（NCP）的新算法，旨在提升基于CP的深度分类器不确定性量化效率（即缩减预测集大小）。NCP的核心思想是利用神经网络学习到的表示，为给定测试输入识别k个最近邻校准样本，并根据其距离赋予重要性权重，从而构建自适应预测集。我们从理论上证明：若神经网络学习到的数据表示满足某些温和条件，NCP将生成比传统CP算法更小的预测集。在CIFAR-10、CIFAR-100和ImageNet数据集上，使用多种深度神经网络进行的全面实验强有力地表明，与先前的CP方法相比，NCP显著减少了预测集大小。