In real-world datasets, noisy labels are pervasive. The challenge of learning with noisy labels (LNL) is to train a classifier that discerns the actual classes from given instances. For this, the model must identify features indicative of the authentic labels. While research indicates that genuine label information is embedded in the learned features of even inaccurately labeled data, it's often intertwined with noise, complicating its direct application. Addressing this, we introduce channel-wise contrastive learning (CWCL). This method distinguishes authentic label information from noise by undertaking contrastive learning across diverse channels. Unlike conventional instance-wise contrastive learning (IWCL), CWCL tends to yield more nuanced and resilient features aligned with the authentic labels. Our strategy is twofold: firstly, using CWCL to extract pertinent features to identify cleanly labeled samples, and secondly, progressively fine-tuning using these samples. Evaluations on several benchmark datasets validate our method's superiority over existing approaches.

翻译：在现实世界的数据集中，噪声标签普遍存在。含噪标签学习（LNL）的挑战在于训练一个能够从给定实例中辨别真实类别的分类器。为此，模型必须识别出指示真实标签的特征。研究表明，即便在不准确标注的数据中，真实标签信息也隐含于学习到的特征中，但这些特征常与噪声交织，难以直接应用。针对这一问题，我们提出通道级对比学习（CWCL）方法。该方法通过在不同通道间进行对比学习，将真实标签信息与噪声区分开来。与传统实例级对比学习（IWCL）不同，CWCL倾向于生成与真实标签对齐的更细腻且鲁棒的特征。我们的策略包含两个步骤：首先，利用CWCL提取相关特征以识别干净标注样本；其次，使用这些样本逐步进行微调。在多个基准数据集上的评估验证了我们的方法优于现有技术。