In recent years, deep neural networks (DNNs) have gained remarkable achievement in computer vision tasks, and the success of DNNs often depends greatly on the richness of data. However, the acquisition process of data and high-quality ground truth requires a lot of manpower and money. In the long, tedious process of data annotation, annotators are prone to make mistakes, resulting in incorrect labels of images, i.e., noisy labels. The emergence of noisy labels is inevitable. Moreover, since research shows that DNNs can easily fit noisy labels, the existence of noisy labels will cause significant damage to the model training process. Therefore, it is crucial to combat noisy labels for computer vision tasks, especially for classification tasks. In this survey, we first comprehensively review the evolution of different deep learning approaches for noisy label combating in the image classification task. In addition, we also review different noise patterns that have been proposed to design robust algorithms. Furthermore, we explore the inner pattern of real-world label noise and propose an algorithm to generate a synthetic label noise pattern guided by real-world data. We test the algorithm on the well-known real-world dataset CIFAR-10N to form a new real-world data-guided synthetic benchmark and evaluate some typical noise-robust methods on the benchmark.

翻译：近年来，深度神经网络（DNN）在计算机视觉任务中取得了显著成就，而DNN的成功往往高度依赖于数据的丰富性。然而，数据及高质量真值的采集过程需要耗费大量人力和资金。在漫长而繁琐的数据标注过程中，标注者容易出错，导致图像标签不正确，即产生噪声标签。噪声标签的出现是不可避免的。此外，由于研究表明DNN容易拟合噪声标签，其存在将对模型训练过程造成严重损害。因此，在计算机视觉任务中，尤其是分类任务中，应对噪声标签至关重要。本综述首先全面回顾了图像分类任务中用于应对噪声标签的不同深度学习方法的发展历程。同时，我们回顾了为设计鲁棒算法而提出的不同噪声模式。进一步，我们探索了真实世界标签噪声的内在规律，并提出了一种基于真实数据生成合成噪声模式的算法。我们在公认的真实世界数据集CIFAR-10N上测试了该算法，构建了一个全新的基于真实数据引导的合成基准，并在该基准上评估了若干典型的噪声鲁棒方法。