Training deep neural networks(DNN) with noisy labels is challenging since DNN can easily memorize inaccurate labels, leading to poor generalization ability. Recently, the meta-learning based label correction strategy is widely adopted to tackle this problem via identifying and correcting potential noisy labels with the help of a small set of clean validation data. Although training with purified labels can effectively improve performance, solving the meta-learning problem inevitably involves a nested loop of bi-level optimization between model weights and hyper-parameters (i.e., label distribution). As compromise, previous methods resort to a coupled learning process with alternating update. In this paper, we empirically find such simultaneous optimization over both model weights and label distribution can not achieve an optimal routine, consequently limiting the representation ability of backbone and accuracy of corrected labels. From this observation, a novel multi-stage label purifier named DMLP is proposed. DMLP decouples the label correction process into label-free representation learning and a simple meta label purifier. In this way, DMLP can focus on extracting discriminative feature and label correction in two distinctive stages. DMLP is a plug-and-play label purifier, the purified labels can be directly reused in naive end-to-end network retraining or other robust learning methods, where state-of-the-art results are obtained on several synthetic and real-world noisy datasets, especially under high noise levels.

翻译：训练深度神经网络（DNN）时，含噪标签会带来挑战，因为DNN容易记住不准确的标签，导致泛化能力较差。近年来，基于元学习的标签校正策略被广泛采用，通过借助少量干净验证数据识别并校正潜在含噪标签来解决该问题。尽管使用净化后的标签训练能有效提升性能，但解决元学习问题不可避免地涉及模型权重与超参数（即标签分布）之间的双层优化嵌套循环。作为折衷，以往方法采用交替更新的耦合学习过程。本文通过实证发现，对模型权重和标签分布进行同步优化无法达到最优路径，从而限制了主干网络的表示能力与校正标签的准确性。基于这一观察，我们提出了一种名为DMLP的新型多阶段标签净化器。DMLP将标签校正过程解耦为无标签表示学习与简易元标签净化器两个阶段。通过这种方式，DMLP能在两个不同阶段分别专注于提取判别性特征和标签校正。DMLP是一种即插即用的标签净化器，其净化后的标签可直接用于朴素端到端网络重训练或其他鲁棒学习方法中。在多个合成和真实含噪数据集上（尤其是高噪声水平下），该方法达到了最先进的结果。