Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels

Zhanhui Lin; Yanlin Liu; Sanping Zhou

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arXiv · 2603.13894

Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels

Zhanhui Lin·Yanlin Liu·Sanping Zhou

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Abstract

Training deep neural networks with noisy labels remains a significant challenge, often leading to degraded performance. Existing methods for handling label noise typically rely on either transition matrix, noise detection, or meta-learning techniques, but they often exhibit low utilization efficiency of noisy samples and incur high computational costs. In this paper, we propose a self-training label correction framework using decoupled bilevel optimization, where a classifier and neural correction function co-evolve. Leveraging a small clean dataset, our method employs noisy posterior simulation and intermediate features to transfer ground-truth knowledge, forming a closed-loop feedback system that prevents error amplification. Theoretical guarantees underpin the stability of our approach, and extensive experiments on benchmark datasets like CIFAR and Clothing1M confirm state-of-the-art performance with reduced training time, highlighting its practical applicability for learning from noisy labels.

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Zhanhui Lin, Yanlin Liu, Sanping Zhou. 2026-03-14. Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels. https://arxiv.org/abs/2603.13894

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