Data to 7 Oct 2026

Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

arXiv 1802.05300 ↗First posted 14 Feb 2018cs.LG cs.CL cs.CVcited by 216

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Abstract (arXiv)

The growing importance of massive datasets used for deep learning makes robustness to label noise a critical property for classifiers to have. Sources of label noise include automatic labeling, non-expert labeling, and label corruption by data poisoning adversaries. Numerous previous works assume that no source of labels can be trusted. We relax this assumption and assume that a small subset of the training data is trusted. This enables substantial label corruption robustness performance gains. In addition, particularly severe label noise can be combated by using a set of trusted data with clean labels. We utilize trusted data by proposing a loss correction technique that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers. Across vision and natural language processing tasks, we experiment with various label noises at several strengths, and show that our method significantly outperforms existing methods.

Key facts. Cited by 216 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 64; 2022: 54; 2023: 40; 2024: 25; 2025: 15; 2026: 18.

216 papers.

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2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 445
2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 388
2021Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotationscited by 253
2021A Fine-Grained Analysis on Distribution Shiftcited by 171
2022Selective-Supervised Contrastive Learning with Noisy Labelscited by 112
2022Robust Training under Label Noise by Over-parameterizationcited by 93
2021FINE Samples for Learning with Noisy Labelscited by 82
2021Jo-SRC: A Contrastive Approach for Combating Noisy Labelscited by 80
2021Robust Audio-Visual Instance Discriminationcited by 80
2021Sample Selection with Uncertainty of Losses for Learning with Noisy Labelscited by 79
2022UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learningcited by 77
2023Improved Online Conformal Prediction via Strongly Adaptive Online Learningcited by 76

38 more

2022Part-based Pseudo Label Refinement for Unsupervised Person Re-identificationcited by 59
2021Open-set Label Noise Can Improve Robustness Against Inherent Label Noisecited by 58
2021Co-learning: Learning from Noisy Labels with Self-supervisioncited by 50
2021Self-Training with Weak Supervisioncited by 49
2022Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimationcited by 49
2021Hard Sample Aware Noise Robust Learning for Histopathology Image Classificationcited by 48
2022ProMix: Combating Label Noise via Maximizing Clean Sample Utilitycited by 48
2021Deep Learning for Suicide and Depression Identification with Unsupervised Label Correctioncited by 45
2021MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentationcited by 43
2021On Tilted Losses in Machine Learning: Theory and Applicationscited by 42
2022Mitigating Memorization of Noisy Labels by Clipping the Model Predictioncited by 40
2021Faster Meta Update Strategy for Noise-Robust Deep Learningcited by 38
2021Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterioncited by 36
2022Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learningcited by 36
2021Joint Negative and Positive Learning for Noisy Labelscited by 34
2022Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalizationcited by 33
2021Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Networkcited by 30
2022Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasetscited 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
2021Sum of Ranked Range Loss for Supervised Learningcited by 23
2021Learning with Noisy Labels for Robust Point Cloud Segmentationcited by 22
2022Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classificationcited by 21
2022Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-trainingcited by 21
2023Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?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
2021Fairness-Aware PAC Learning from Corrupted Datacited by 18
2022On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classificationcited by 17
2022CTRL: Clustering Training Losses for Label Error Detectioncited by 17
2022Label-Noise Learning with Intrinsically Long-Tailed Datacited by 17
2022Instance-Dependent Noisy Label Learning via Graphical Modellingcited by 17
2023Adaptive Sharpness-Aware Pruning for Robust Sparse Networkscited by 17
2023CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labelscited by 17
2021Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Datacited by 16

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