提出新方法提升有序分类的置信度校准，兼顾顺序一致性和准确性。
Ordinal-Aware Calibration for Ordinal Classification
- 设计统一损失函数ORCU，融合软编码与对数屏障机制。
- 在四个基准上实现最优校准效果，精度未下降。
- 无需修改网络结构，适合需要可靠置信度的场景。
深度神经网络常产生过度自信的错误预测。在有序分类中，预测还需满足单峰且顺序一致的要求，这一要求虽主导以往研究，却忽略了校准问题。本文首次将此双重挑战形式化为有序校准，并提出有序校准与单峰性损失（ORCU）。与增量模块组合不同，ORCU以简洁、原则性的方式结合距离感知软编码与相邻logit差值的有序感知对数屏障扩展。该耦合具有理论协同效应：软编码目标通过提供有限、全支持的有序锚点，预调节优化景观，实现良好修正；而对数屏障扩展在单峰可行内部保持非消失的相邻差值梯度，使屏障能修正置信度而不破坏有序几何结构。ORCU无需架构变更或后处理校准。在四个有序分类基准上，其达到当前最佳校准性能，且不影响精度。我们通过10种专用和通用损失函数建立了可复现的基准，代码已开源。
原文摘要 · Abstract (English)
Deep neural networks frequently produce overconfident, miscalibrated predictions. In ordinal classification, predictions must also adhere to a unimodal and order-consistent structure, a requirement that has dominated prior work while overlooking calibration. We formalize this joint challenge as ordinal calibration for the first time and propose the Ordinal loss for Calibration and Unimodality (ORCU). Unlike incremental modular combinations, ORCU provides a concise, principled unification of distance-aware soft encoding and an ordinal-aware log-barrier extension on adjacent logit gaps. This coupling is theoretically synergetic: soft-encoded targets pre-condition the optimization landscape by providing a finite, full-support ordinal anchor for well-posed refinement, while the log-barrier extension preserves non-vanishing adjacent-gap gradients in the unimodality-feasible interior. This enables the log-barrier to refine confidence without disrupting the ordinal geometry. ORCU requires no architectural changes or post-hoc calibration. Across four ordinal benchmarks, it achieves state-of-the-art calibration without compromising accuracy. We establish a new reliability standard by providing a reproducible benchmark over 10 specialized and general-purpose loss functions. Our code is available at https://github.com/labhai/ORCU.
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