Noise-Aware Framework for Correcting Corrupted Labels

2026-06-10

AI Summary

Label noise in real-world datasets severely degrades model performance. To address this issue, this work proposes the CANOLA framework, which uniquely integrates explicit noise distribution modeling with progressive soft label correction. CANOLA employs a noise-aware deep neural network to estimate the underlying noise distribution and iteratively refines soft labels during training in a cautious manner, thereby avoiding premature or erroneous corrections that compromise stability and efficacy. Extensive experiments demonstrate that CANOLA significantly outperforms existing methods across six benchmark datasets, achieving relative error reductions of 19%–52%. Moreover, classifiers trained on labels refined by CANOLA surpass even sophisticated models by up to 67% in performance, highlighting the framework’s effectiveness in enhancing downstream learning tasks.

Abstract

High-quality labeled data is essential for training reliable ML/DL models. However, real-world datasets often contain a considerable proportion of corrupted labels, which can severely degrade model performance. To address this problem, we propose CANOLA, a novel framework for correcting corrupted labels through noise-aware learning and iterative label refinement. CANOLA explicitly estimates the underlying noise distribution of the dataset and incorporates this information into the training of a noise-aware Deep Neural Network. By incorporating noise characteristics during learning, CANOLA enables the model to down-weight unreliable supervision signals and focus on trustworthy patterns, thereby improving robustness and generalization. Label correction is performed via cautious, iterative soft label refinement, in which model predictions are blended with observed labels to prevent premature or erroneous updates. This progressive refinement allows the dataset to be repaired in a stable and controlled manner. We evaluate CANOLA on six widely used datasets under realistic noisy labeling scenarios. Experimental results show that CANOLA consistently outperforms SOTA label correction methods, achieving relative improvements ranging from 19% to 52% in error reduction. Moreover, models trained on datasets corrected by CANOLA obtain substantial downstream performance gains. Even simple classifiers trained on CANOLA's corrected data can outperform complex model-centric approaches by margins of up to 67%.

Problem

• corrupted labels
• label noise
• noise-aware learning
• label correction
• noisy labeling

Innovation

• noise-aware learning
• label correction
• iterative refinement
• noisy labels
• robust deep learning