mlsanityData to 7 Oct 2026

Learning from Noisy Labels with Deep Neural Networks: A Survey

arXiv 2007.08199 ↗First posted 16 Jul 2020cs.LG cs.CV stat.MLcited by 580

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Abstract (arXiv)

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.

Key facts. Cited by 580 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jul 2020.

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Citing papers per year: 2021: 74; 2022: 109; 2023: 119; 2024: 114; 2025: 85; 2026: 79.

580 papers.

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  12. 2021MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluationcited by 86
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  9. 2022DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotationscited by 54
  10. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  11. 2021Interactive Label Cleaning with Example-based Explanationscited by 48
  12. 2021Sample Selection for Fair and Robust Trainingcited by 48
  13. 2021Hard Sample Aware Noise Robust Learning for Histopathology Image Classificationcited by 48
  14. 2021Asymmetric Loss Functions for Learning with Noisy Labelscited by 46
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  16. 2023When Source-Free Domain Adaptation Meets Learning with Noisy Labelscited by 46
  17. 2021Deep Learning for Suicide and Depression Identification with Unsupervised Label Correctioncited by 45
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  22. 2024Impact of Preference Noise on the Alignment Performance of Generative Language Modelscited by 41
  23. 2025Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifierscited by 40
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  30. 2021Data Efficient Language-supervised Zero-shot Recognition with Optimal Transport Distillationcited by 35
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  32. 2023A Path to Simpler Models Starts With Noisecited by 33
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  37. 2022To Aggregate or Not? Learning with Separate Noisy Labelscited by 29
  38. 2022Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrievalcited by 27

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