Learning From Noisy Labels With Deep Neural Networks: A Survey

Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data. 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.

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• 1 However, the quality of data labels is a concern because of the lack of high-quality labels in many real-world scenarios.
• 2 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.
• 3 In this survey, we first describe the problem of learning with label noise from a supervised learning perspective.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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