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Learning with Bounded Instance- and Label-dependent Label Noise

arXiv 1709.03768 ↗First posted 12 Sep 2017stat.ML cs.LGcited by 57

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Abstract (arXiv)

Instance- and Label-dependent label Noise (ILN) widely exists in real-world datasets but has been rarely studied. In this paper, we focus on Bounded Instance- and Label-dependent label Noise (BILN), a particular case of ILN where the label noise rates -- the probabilities that the true labels of examples flip into the corrupted ones -- have upper bound less than 1. Specifically, we introduce the concept of distilled examples, i.e. examples whose labels are identical with the labels assigned for them by the Bayes optimal classifier, and prove that under certain conditions classifiers learnt on distilled examples will converge to the Bayes optimal classifier. Inspired by the idea of learning with distilled examples, we then propose a learning algorithm with theoretical guarantees for its robustness to BILN. At last, empirical evaluations on both synthetic and real-world datasets show effectiveness of our algorithm in learning with BILN.

Key facts. Cited by 57 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Sep 2017.

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Citing papers per year: 2021: 24; 2022: 9; 2023: 11; 2024: 6; 2025: 3; 2026: 4.

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  1. 2021Learning with Feature-Dependent Label Noise: A Progressive Approachcited by 102
  2. 2021Sample Selection with Uncertainty of Losses for Learning with Noisy Labelscited by 79
  3. 2021Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularizationcited by 62
  4. 2023Towards Label-free Scene Understanding by Vision Foundation Modelscited by 54
  5. 2021Instance-dependent Label-noise Learning under a Structural Causal Modelcited by 50
  6. 2021Asymmetric Loss Functions for Learning with Noisy Labelscited by 46
  7. 2021Active label cleaning for improved dataset quality under resource constraintscited by 44
  8. 2021Learning from Noisy Labels for Entity-Centric Information Extractioncited by 34
  9. 2022Identifiability of Label Noise Transition Matrixcited by 34
  10. 2022Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalizationcited by 33
  11. 2021Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Networkcited by 30
  12. 2021Understanding Instance-Level Label Noise: Disparate Impacts and Treatmentscited by 22
38 more
  1. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 22
  2. 2023FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Datacited by 21
  3. 2021Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrectioncited by 20
  4. 2023Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labelscited by 20
  5. 2022SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentationcited by 17
  6. 2021Analysing the Noise Model Error for Realistic Noisy Label Datacited by 14
  7. 2021Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficialcited by 13
  8. 2023InstanT: Semi-supervised Learning with Instance-dependent Thresholdscited by 13
  9. 2021Tackling Instance-Dependent Label Noise via a Universal Probabilistic Modelcited by 12
  10. 2022Trustable Co-label Learning from Multiple Noisy Annotatorscited by 12
  11. 2023Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowdscited by 11
  12. 2021EQFace: A Simple Explicit Quality Network for Face Recognitioncited by 10
  13. 2022Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labelscited by 8
  14. 2023Regularly Truncated M-estimators for Learning with Noisy Labelscited by 8
  15. 2021Instance Correction for Learning with Open-set Noisy Labelscited by 7
  16. 2022Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learningcited by 7
  17. 2024Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learningcited by 7
  18. 2024Mitigating Label Noise on Graph via Topological Sample Selectioncited by 6
  19. 2021Confidence Adaptive Regularization for Deep Learning with Noisy Labelscited by 5
  20. 2021Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awarenesscited by 4
  21. 2021Binary classification with corrupted labelscited by 4
  22. 2023Leveraging an Alignment Set in Tackling Instance-Dependent Label Noisecited by 4
  23. 2024Improving Label Error Detection and Elimination with Uncertainty Quantificationcited by 4
  24. 2021Approximating Instance-Dependent Noise via Instance-Confidence Embeddingcited by 3
  25. 2021Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimationcited by 3
  26. 2024Multiclass Learning from Noisy Labels for Non-decomposable Performance Measurescited by 3
  27. 2024Learning Causal Transition Matrix for Instance-dependent Label Noisecited by 3
  28. 2021Collaborative Label Correction via Entropy Thresholdingcited by 2
  29. 2022Do We Need to Penalize Variance of Losses for Learning with Label Noise?cited by 2
  30. 2022Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibrationcited by 2
  31. 2023Binary Classification with Instance and Label Dependent Label Noisecited by 2
  32. 2023Generating the Ground Truth: Synthetic Data for Soft Label and Label Noise Researchcited by 2
  33. 2025GeoT: Geometry-guided Instance-dependent Transition Matrix for Semi-supervised Tooth Point Cloud Segmentationcited by 2
  34. 2026Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Predictioncited by 2
  35. 2022What killed the Convex Booster ?cited by 1
  36. 2023LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labelscited by 1
  37. 2023Software Entity Recognition with Noise-Robust Learningcited by 1
  38. 2021A Symmetric Loss Perspective of Reliable Machine Learningcited by 0

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