Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels
This addresses the challenge of learning from noisy labels for machine learning practitioners, offering a practical solution with improved efficiency.
The paper tackles the problem of training deep neural networks with noisy labels, which degrades performance, by proposing a self-training label correction framework using decoupled bilevel optimization that achieves state-of-the-art results on benchmarks like CIFAR and Clothing1M with reduced training time.
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.