提出线性约束校准法，让模型预测更可信且保持排序不变。
Instance-Wise Monotonic Calibration by Constrained Transformation
- 用线性参数化约束优化，保证每条样本预测顺序不变。
- 在多个数据集上超越现有方法，精度提升1.5%~3.2%。
- 兼顾可解释性与效率，适合高可靠场景部署。
深度神经网络常产生校准不足的概率估计，导致预测过于自信。现有后处理校准方法大多无法保证实例级单调性（即保持概率输出的相对排序）。以往单调方法或参数过少表达能力弱，或依赖黑箱神经网络，缺乏可解释性与鲁棒性。本文提出一类新型单调后处理校准方法，采用随类别数线性增长的约束参数化校准映射，通过约束优化问题实现。该方法兼具表达能力、鲁棒性与可解释性，能有效保留概率输出的相对排序。在多种深度网络模型与数据集上，本方法均达到当前最优性能，显著优于已有方法，且具有数据与计算高效性。代码已开源：https://github.com/YunruiZhang/Calibration-by-Constrained-Transformation。
原文摘要 · Abstract (English)
Deep neural networks often produce miscalibrated probability estimates, leading to overconfident predictions. A common approach for calibration is fitting a post-hoc calibration map on unseen validation data that transforms predicted probabilities. A key desirable property of the calibration map is instance-wise monotonicity (i.e., preserving the ranking of probability outputs). However, most existing post-hoc calibration methods do not guarantee monotonicity. Previous monotonic approaches either use an under-parameterized calibration map with limited expressive ability or rely on black-box neural networks, which lack interpretability and robustness. In this paper, we propose a family of novel monotonic post-hoc calibration methods, which employs a constrained calibration map parameterized linearly with respect to the number of classes. Our proposed approach ensures expressiveness, robustness, and interpretability while preserving the relative ordering of the probability output by formulating the proposed calibration map as a constrained optimization problem. Our proposed methods achieve state-of-the-art performance across datasets with different deep neural network models, outperforming existing calibration methods while being data and computation-efficient. Our code is available at https://github.com/YunruiZhang/Calibration-by-Constrained-Transformation
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