  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.
