Data to 7 Oct 2026

Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

arXiv 1905.05040 ↗First posted 13 May 2019cs.LG stat.MLcited by 209

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Abstract (arXiv)

Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly training deep neural networks (DNNs) as DNNs usually have the high capacity to memorize the noisy labels. In this paper, we find that the test accuracy can be quantitatively characterized in terms of the noise ratio in datasets. In particular, the test accuracy is a quadratic function of the noise ratio in the case of symmetric noise, which explains the experimental findings previously published. Based on our analysis, we apply cross-validation to randomly split noisy datasets, which identifies most samples that have correct labels. Then we adopt the Co-teaching strategy which takes full advantage of the identified samples to train DNNs robustly against noisy labels. Compared with extensive state-of-the-art methods, our strategy consistently improves the generalization performance of DNNs under both synthetic and real-world training noise.

Key facts. Cited by 209 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 48; 2022: 50; 2023: 40; 2024: 34; 2025: 22; 2026: 15.

209 papers.

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2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 388
2022Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learntcited by 233
2023DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Modelscited by 162
2021Deep Learning with Label Differential Privacycited by 113
2022Selective-Supervised Contrastive Learning with Noisy Labelscited by 112
2021Augmentation Strategies for Learning with Noisy Labelscited by 86
2021Sample Selection with Uncertainty of Losses for Learning with Noisy Labelscited by 79
2021Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labelscited by 69
2021Learning Fast Sample Re-weighting Without Reward Datacited by 69
2021NGC: A Unified Framework for Learning with Open-World Noisy Datacited by 58
2022Hyperbolic Contrastive Learning for Visual Representations beyond Objectscited by 58
2021Co-learning: Learning from Noisy Labels with Self-supervisioncited by 50

38 more

2021Sample Selection for Fair and Robust Trainingcited by 48
2021Hard Sample Aware Noise Robust Learning for Histopathology Image Classificationcited by 48
2022Investigating Why Contrastive Learning Benefits Robustness Against Label Noisecited by 46
2021LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environmentcited by 40
2021Faster Meta Update Strategy for Noise-Robust Deep Learningcited by 38
2022Characterizing Datapoints via Second-Split Forgettingcited by 37
2023Self-Influence Guided Data Reweighting for Language Model Pre-trainingcited by 37
2021Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterioncited by 36
2021Robust Long-Tailed Learning under Label Noisecited by 35
2023Twin Contrastive Learning with Noisy Labelscited by 28
2024Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Expertscited by 28
2021Continual Learning on Noisy Data Streams via Self-Purified Replaycited by 27
2022CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learningcited by 24
2022SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labelscited by 24
2021Understanding Instance-Level Label Noise: Disparate Impacts and Treatmentscited by 22
2022Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-trainingcited by 21
2022When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Methodcited by 21
2023Learning from Noisy Labels with Decoupled Meta Label Purifiercited by 21
2024Why is SAM Robust to Label Noise?cited by 21
2021Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrectioncited by 20
2022Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantificationcited by 20
2023Understanding Self-Distillation in the Presence of Label Noisecited by 20
2023Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracycited by 20
2022Identifying Hard Noise in Long-Tailed Sample Distributioncited by 19
2021Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noisecited by 18
2021Memorization and Generalization in Neural Code Intelligence Modelscited by 17
2021PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labelscited by 17
2022CTRL: Clustering Training Losses for Label Error Detectioncited by 17
2023Late Stopping: Avoiding Confidently Learning from Mislabeled Examplescited by 17
2023CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labelscited by 17
2022Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labelscited by 16
2023Identifying Label Errors in Object Detection Datasets by Loss Inspectioncited by 16
2023BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learningcited by 16
2023Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discoverycited by 16
2022Scalable Penalized Regression for Noise Detection in Learning with Noisy Labelscited by 15
2022Robustness to Label Noise Depends on the Shape of the Noise Distribution in Feature Spacecited by 15
2021Analysing the Noise Model Error for Realistic Noisy Label Datacited by 14
2021ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learningcited by 13

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