mlsanityData to 7 Oct 2026

Co-learning: Learning from Noisy Labels with Self-supervision

arXiv 2108.04063 ↗First posted 5 Aug 2021cs.LGcited by 50

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Abstract (arXiv)

Noisy labels, resulting from mistakes in manual labeling or webly data collecting for supervised learning, can cause neural networks to overfit the misleading information and degrade the generalization performance. Self-supervised learning works in the absence of labels and thus eliminates the negative impact of noisy labels. Motivated by co-training with both supervised learning view and self-supervised learning view, we propose a simple yet effective method called Co-learning for learning with noisy labels. Co-learning performs supervised learning and self-supervised learning in a cooperative way. The constraints of intrinsic similarity with the self-supervised module and the structural similarity with the noisily-supervised module are imposed on a shared common feature encoder to regularize the network to maximize the agreement between the two constraints. Co-learning is compared with peer methods on corrupted data from benchmark datasets fairly, and extensive results are provided which demonstrate that Co-learning is superior to many state-of-the-art approaches.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). Builds on 38 of its 45 references that are in the map. First posted on arXiv 5 Aug 2021.

Builds on

38 of 45 references.

References · 38

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  3. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  5. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  6. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  7. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  8. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
  9. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
  10. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  11. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
  12. 2016Colorful Image Colorizationcited by 1,113
26 more
  1. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,053
  2. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 994
  3. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
  4. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
  5. 2017MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labelscited by 652
  6. 2018Exploring the Limits of Weakly Supervised Pretrainingcited by 604
  7. 2019Symmetric Cross Entropy for Robust Learning with Noisy Labelscited by 499
  8. 2019How does Disagreement Help Generalization against Label Corruption?cited by 390
  9. 2019Unsupervised Label Noise Modeling and Loss Correctioncited by 327
  10. 2014Training Deep Neural Networks on Noisy Labels with Bootstrappingcited by 322
  11. 2018Joint Optimization Framework for Learning with Noisy Labelscited by 293
  12. 2020Combating noisy labels by agreement: A joint training method with co-regularizationcited by 289
  13. 2020Normalized Loss Functions for Deep Learning with Noisy Labelscited by 257
  14. 2015YFCC100M: The New Data in Multimedia Researchcited by 234
  15. 2017Decoupling "when to update" from "how to update"cited by 230
  16. 2018Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noisecited by 216
  17. 2019Understanding and Utilizing Deep Neural Networks Trained with Noisy Labelscited by 209
  18. 2017CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noisecited by 181
  19. 2019SELF: Learning to Filter Noisy Labels with Self-Ensemblingcited by 164
  20. 2018Learning to Learn from Noisy Labeled Datacited by 160
  21. 2019Learning From Noisy Labels By Regularized Estimation Of Annotator Confusioncited by 126
  22. 2019Deep Self-Learning From Noisy Labelscited by 122
  23. 2021Learning with Feature-Dependent Label Noise: A Progressive Approachcited by 102
  24. 2018A Semi-Supervised Two-Stage Approach to Learning from Noisy Labelscited by 29
  25. 2019A Novel Self-Supervised Re-labeling Approach for Training with Noisy Labelscited by 10
  26. 2020Markov-Lipschitz Deep Learningcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 8; 2023: 9; 2024: 14; 2025: 11; 2026: 7.

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  1. 2022SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentationcited by 171
  2. 2022Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learningcited by 84
  3. 2021ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learningcited by 72
  4. 2023CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignmentcited by 44
  5. 2024Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactionscited by 43
  6. 2026INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrievalcited by 26
  7. 2022Identifying Hard Noise in Long-Tailed Sample Distributioncited by 19
  8. 2023DealMVC: Dual Contrastive Calibration for Multi-view Clusteringcited by 19
  9. 2024Self-supervised Learning for Electroencephalogram: A Systematic Surveycited by 19
  10. 2022Hyperspherical Consistency Regularizationcited by 16
  11. 2023SemiReward: A General Reward Model for Semi-supervised Learningcited by 14
  12. 2022Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labelscited by 13
38 more
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  2. 2023Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Frameworkcited by 11
  3. 2023ALEX: Towards Effective Graph Transfer Learning with Noisy Labelscited by 11
  4. 2025Ambiguity-Restrained Text-Video Representation Learning for Partially Relevant Video Retrievalcited by 10
  5. 2023Dual-view Curricular Optimal Transport for Cross-lingual Cross-modal Retrievalcited by 9
  6. 2023Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designercited by 8
  7. 2024RobGC: Towards Robust Graph Condensationcited by 8
  8. 2022Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classificationcited by 6
  9. 2025Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimizationcited by 5
  10. 2025An Analysis of Concept Bottleneck Models: Measuring, Understanding, and Mitigating the Impact of Noisy Annotationscited by 5
  11. 2025Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examplescited by 4
  12. 2025ChronoSelect: Robust Learning with Noisy Labels via Dynamics Temporal Memorycited by 4
  13. 2022Tripartite: Tackle Noisy Labels by a More Precise Partitioncited by 3
  14. 2023Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?cited by 3
  15. 2024D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognitioncited by 3
  16. 2022Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibrationcited by 2
  17. 2024EEG-MACS: Manifold Attention and Confidence Stratification for EEG-based Cross-Center Brain Disease Diagnosis under Unreliable Annotationscited by 2
  18. 2025Classifying Long-tailed and Label-noise Data via Disentangling and Unlearningcited by 2
  19. 2024Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classificationcited by 1
  20. 2024Cross-Platform Hate Speech Detection with Weakly Supervised Causal Disentanglementcited by 1
  21. 2024Combating Semantic Contamination in Learning with Label Noisecited by 1
  22. 2025Pseudo-Labeling Driven Refinement of Benchmark Object Detection Datasets via Analysis of Learning Patternscited by 1
  23. 2025Benchmarking noisy label detection methodscited by 1
  24. 2026PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documentscited by 1
  25. 2023Eye tracking guided deep multiple instance learning with dual cross-attention for fundus disease detectioncited by 0
  26. 2024CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidancecited by 0
  27. 2024Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Raritycited by 0
  28. 2024Group Benefits Instances Selection for Data Purificationcited by 0
  29. 2024Extremely Fine-Grained Visual Classification over Resembling Glyphs in the Wildcited by 0
  30. 2024Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrastingcited by 0
  31. 2025Collaborative Learning of Scattering and Deep Features for SAR Target Recognition with Noisy Labelscited by 0
  32. 2025Local K-Similarity Constraint for Federated Learning with Label Noisecited by 0
  33. 2025Weak-to-Strong Generalization Enables Fully Automated De Novo Training of Multi-head Mask-RCNN Model for Segmenting Densely Overlapping Cell Nuclei in Multiplex Whole-slice Brain Imagescited by 0
  34. 2026Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noisecited by 0
  35. 2026Robust Fuzzy Multi-view Learning under View Conflictcited by 0
  36. 2026OSCS-SupCon: Orthogonal Sigmoid-based Common and Style Supervised Contrastive Learning for Robust Feature Disentanglementcited by 0
  37. 2026Leveraging Dissimilarity Invariance as a Robust Anchor for Learning with Noisy Labelscited by 0
  38. 2026Stop Replacing Noise with Noise: Two-Source Reliability Assessment for Label Correction and Sample Reweighting in Label-Noise Learningcited by 0