让模型按类别自适应调整预测置信区间，更精准又不丢失可靠性。
Class Adaptive Conformal Training
- 基于增广拉格朗日法，自动学习每类别的最优预测集形状。
- 在多个数据集上实现更小的预测集，且覆盖率达标。
- 无需假设数据分布，适合图像和文本分类场景。
深度神经网络在各类任务中表现卓越，但其概率估计常不可靠，导致预测过度自信。共形预测（Conformal Prediction, CP）提供了严格的不确定性量化框架，能生成具有覆盖保证的预测集。现有方法优化整体预测集大小，但难以实现类别条件下的精细调控，通常需依赖数据分布先验。本文提出类别自适应共形训练（Class Adaptive Conformal Training, CaCT），将共形训练建模为增广拉格朗日优化问题，无需任何分布假设即可自适应地学习类别条件下的预测集形状。在多个基准数据集上的实验表明，包括标准与长尾图像识别及文本分类任务，CaCT始终优于现有方法，在保持所需覆盖率的同时，显著缩小预测集并提升信息量。
原文摘要 · Abstract (English)
Deep neural networks have achieved remarkable success across a variety of tasks, yet they often suffer from unreliable probability estimates. As a result, they can be overconfident in their predictions. Conformal Prediction (CP) offers a principled framework for uncertainty quantification, yielding prediction sets with rigorous coverage guarantees. Existing conformal training methods optimize for overall set size, but shaping the prediction sets in a class-conditional manner is not straightforward and typically requires prior knowledge of the data distribution. In this work, we introduce Class Adaptive Conformal Training (CaCT), which formulates conformal training as an augmented Lagrangian optimization problem that adaptively learns to shape prediction sets class-conditionally without making any distributional assumptions. Experiments on multiple benchmark datasets, including standard and long-tailed image recognition as well as text classification, demonstrate that CaCT consistently outperforms prior conformal training methods, producing significantly smaller and more informative prediction sets while maintaining the desired coverage guarantees.
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