In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise. To better distinguish these noise types and utilize their semantics, we propose a novel sample selection-based approach for noisy label learning, called Proto-semi. Proto-semi initially divides all samples into the confident and unconfident datasets via warm-up. By leveraging the confident dataset, prototype vectors are constructed to capture class characteristics. Subsequently, the distances between the unconfident samples and the prototype vectors are calculated to facilitate noise classification. Based on these distances, the labels are either corrected or retained, resulting in the refinement of the confident and unconfident datasets. Finally, we introduce a semi-supervised learning method to enhance training. Empirical evaluations on a real-world annotated dataset substantiate the robustness of Proto-semi in handling the problem of learning from noisy labels. Meanwhile, the prototype-based repartitioning strategy is shown to be effective in mitigating the adverse impact of label noise. Our code and data are available at https://github.com/fuxiAIlab/ProtoSemi.

翻译：在本文中，我们研究了真实标注场景下的噪声标签学习问题，其中噪声可分为两类：事实性噪声和模糊性噪声。为更好地区分这些噪声类型并利用其语义，我们提出了一种基于样本选择的新型噪声标签学习方法，称为Proto-semi。该方法首先通过预热将所有样本划分为高置信度数据集和低置信度数据集。利用高置信度数据集，我们构建了用于捕获类别特征的原型向量。随后，计算低置信度样本与原型向量之间的距离，以促进噪声分类。基于这些距离，对标签进行修正或保留，从而优化高置信度与低置信度数据集的划分。最后，我们引入半监督学习方法以增强训练效果。在真实标注数据集上的实验评估证实了Proto-semi在解决噪声标签学习问题中的鲁棒性。同时，基于原型的重划分策略被证明能有效减轻标签噪声的不利影响。我们的代码和数据可在https://github.com/fuxiAIlab/ProtoSemi获取。