English(EN) Noisy-Label Calibration with Multiple Unreliable Labels

新方法改进了具有噪声标签的AI模型校准

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了新的方法来评估和重新校准在处理多个不可靠标签时的二元预测器，这是医学标注和众包等领域中常见的问题。提出的噪声校正ECE（NC-ECE）估计器在类别条件噪声下准确测量了期望校准误差，并对其偏差进行了推导界限。还介绍了用于估计未知噪声概率和事后重新校准的实用算法，与不考虑标签噪声的方法相比，这些算法可显著提高校准精度。 AI

影响提高了在不完美、真实世界数据上训练的AI模型的可靠性。

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arXiv cs.LG TIER_1 English(EN) · Keisuke Hanada, Futoshi Futami · 2026-10-15 04:00

具有多个不可靠标签的噪声标签校准

arXiv:2610.19543v1 Announce Type: cross Abstract: In medical annotation and crowdsourcing, multiple annotators assign labels to each example, but these labels may be incorrect. Treating such noisy labels as ground truth can bias calibration evaluation. To address this problem, we…

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具有多个不可靠标签的噪声标签校准

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