Traditional methods for learning with the presence of noisy labels have successfully handled datasets with artificially injected noise but still fall short of adequately handling real-world noise. With the increasing use of meta-learning in the diverse fields of machine learning, researchers leveraged auxiliary small clean datasets to meta-correct the training labels. Nonetheless, existing meta-label correction approaches are not fully exploiting their potential. In this study, we propose an Enhanced Meta Label Correction approach abbreviated as EMLC for the learning with noisy labels (LNL) problem. We re-examine the meta-learning process and introduce faster and more accurate meta-gradient derivations. We propose a novel teacher architecture tailored explicitly to the LNL problem, equipped with novel training objectives. EMLC outperforms prior approaches and achieves state-of-the-art results in all standard benchmarks. Notably, EMLC enhances the previous art on the noisy real-world dataset Clothing1M by $1.52\%$ while requiring $\times 0.5$ the time per epoch and with much faster convergence of the meta-objective when compared to the baseline approach.

翻译：传统方法在处理带噪标签的学习问题时，虽能成功应对人工注入噪声的数据集，但在处理真实世界噪声时仍存在不足。随着元学习在机器学习各领域的广泛应用，研究者借助少量辅助干净数据集对训练标签进行元修正。然而，现有元标签修正方法尚未充分挖掘其潜力。本研究提出增强型元标签修正方法（简称EMLC），用于解决带噪标签学习（LNL）问题。我们重新审视元学习过程，引入更快速、更准确的元梯度推导方法。针对LNL问题，我们专门设计了一种新型教师架构，并配备创新性训练目标。EMLC在所有标准基准测试中均超越先前方法，达到最优性能。值得注意的是，在含真实噪声的Clothing1M数据集上，EMLC较先前最优方法提升了1.52%，同时每轮训练时间仅为基准方法的0.5倍，且元目标收敛速度显著更快。