Radial-Angular Geometry for Reliable Update Diagnosis in Noisy-Label Learning

• URL: http://arxiv.org/abs/2605.17429v1
• Date: Sun, 17 May 2026 12:50:16 GMT
• Title: Radial-Angular Geometry for Reliable Update Diagnosis in Noisy-Label Learning
• Abstract summary: We recast reliability estimation as diagnosis of the observed-label update.<n>Relative Geometric Conflict helps distinguish large but aligned hard-clean updates from large conflicting updates caused by corrupted labels.
• Score: 3.6771547306155523
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Noisy-label methods often estimate sample reliability from forward-space signals such as loss, confidence, or entropy. These signals indicate whether a sample is difficult to predict, but they do not directly test whether its observed label induces a reliable parameter update. This gap matters because hard clean samples and mislabeled samples can have similar loss while inducing different updates. We recast reliability estimation as diagnosis of the observed-label update. The sample-wise empirical Fisher trace gives a backward-space measure of update energy: for the classifier layer, it factorizes into a prediction-residual term and a feature-sensitivity term, so it captures information beyond scalar loss. Trace, however, is still a radial magnitude signal and cannot decide whether a large update is useful or harmful. We therefore propose Relative Geometric Conflict (RGC), which compares the observed-label gradient with a reference gradient induced by an EMA teacher. The conflict term helps distinguish large but aligned hard-clean updates from large conflicting updates caused by corrupted labels. Across synthetic and real-world noisy-label benchmarks, RGC improves hard-clean preservation and accuracy under our evaluation protocol.

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