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：传统学习方法在处理含噪标签时，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明显不足。随着元学习在机器学习各领域的广泛应用，研究者利用少量辅助干净数据集对训练标签进行元校正。然而，现有元标签校正方法未能充分发挥其潜力。本研究提出增强型元标签校正方法（简称EMLC），用于解决含噪标签学习（LNL）问题。我们重新审视元学习过程，引入更快更精确的元梯度推导方法，并针对LNL问题设计新型教师架构及训练目标。实验表明，EMLC在所有标准基准测试中均超越现有方法，达到最新最优性能。值得注意的是，在真实噪声数据集Clothing1M上，EMLC较之前最优方法提升$1.52\%$，同时每轮训练时间仅为基准方法的$\times 0.5$倍，且元目标收敛速度显著加快。