Stop Replacing Noise with Noise: Two-Source Reliability Assessment for Label Correction and Sample Reweighting in Label-Noise Learning

2026-08-04

AI Summary

This work addresses a critical limitation in existing reconstruction-based methods for learning with noisy labels: their tendency to jointly assess the reliability of observed labels and pseudo-targets, which often leads to unreliable signals substituting one another and hinders effective denoising. To overcome this, the paper proposes TRACE, a novel framework that decouples the reliability evaluation of these two sources for the first time. Specifically, it evaluates observed labels through loss fitting, shallow-feature relational stability, and prediction consistency, while assessing pseudo-targets via model confidence. These independent reliability estimates are then used to separately govern label correction and sample reweighting. By preventing error propagation, TRACE generates more trustworthy pseudo-supervision, significantly outperforming current reconstruction-based approaches across multiple synthetic and real-world noisy benchmarks, and thereby enhancing model robustness and generalization.

Abstract

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.

Problem

• label-noise learning
• noisy labels
• pseudo targets
• reliability assessment
• sample reweighting

Innovation

• label-noise learning
• two-source reliability assessment
• pseudo-label correction
• sample reweighting
• representation diagnostics