AI 中文总结
针对标签噪声学习中重构方法的隐式耦合问题，提出TRACE双源可靠性评估框架，分别评估观测标签与伪目标，提升了带噪声基准上的重构基线性能与伪监督可靠性。
AI 中文摘要
基于重构的带噪声标签学习方法会将观测标签与模型生成的伪目标混合，通常用一个样本级清洁度分数控制两个分支，这会产生隐式耦合：降低对观测标签的信任度会自动提升对伪目标的信任度。我们表明，这种互补性可能会用另一个不可靠信号替换当前不可靠信号，因为从损坏的监督中学习到的伪目标可能会重现其本应纠正的噪声。我们的表征诊断对这种不匹配提供了一致解释：带噪声的监督会更强地引导深层网络层，而浅层关系则相对稳定，能提供损失后验之外的信息。因此，我们提出TRACE，即用于标签校正与样本重加权的双源可靠性评估框架。TRACE通过损失拟合、浅层关系稳定性和预测一致性评估观测标签，同时用模型置信度单独评估伪目标，其特定源分数控制目标校正和监督强度，无需假设互补可靠性。在合成和真实世界带噪声基准上，TRACE提升了代表性的重构基线，并生成了更可靠的伪监督。
英文摘要
Refurbishment-based noisy-label learning mixes an observed label with a model-derived pseudo target, typically using one sample-wise cleanliness score to control both branches. This creates a hidden coupling: reducing trust in the observed label automatically increases trust in the pseudo target. We show that this complementarity can replace one unreliable signal with another because a pseudo target learned from corrupted supervision may reproduce the noise it is meant to correct. Our representation diagnostics provide a consistent account of this mismatch: noisy supervision redirects deeper layers more strongly, whereas shallower relations remain comparatively stable and provide information beyond the loss posterior. We therefore propose TRACE, a Two-Source Reliability Assessment framework for Label Correction and Sample Reweighting. TRACE assesses the observed label using loss fit, shallow relation stability, and prediction agreement, while separately assessing the pseudo target using model confidence. Its source-specific scores control target correction and supervision strength without assuming complementary reliability. Across synthetic and real-world noisy benchmarks, TRACE improves representative refurbishment baselines and yields more reliable pseudo supervision.
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