  Deep learning has achieved remarkable success in numerous domains with help
from large amounts of big data. However, the quality of data labels is a
concern because of the lack of high-quality labels in many real-world
scenarios. As noisy labels severely degrade the generalization performance of
deep neural networks, learning from noisy labels (robust training) is becoming
an important task in modern deep learning applications. In this survey, we
first describe the problem of learning with label noise from a supervised
learning perspective. Next, we provide a comprehensive review of 62
state-of-the-art robust training methods, all of which are categorized into
five groups according to their methodological difference, followed by a
systematic comparison of six properties used to evaluate their superiority.
Subsequently, we perform an in-depth analysis of noise rate estimation and
summarize the typically used evaluation methodology, including public noisy
datasets and evaluation metrics. Finally, we present several promising research
directions that can serve as a guideline for future studies. All the contents
will be available at https://github.com/songhwanjun/Awesome-Noisy-Labels.
