mlsanityData to 7 Oct 2026

Label-Noise Learning with Intrinsically Long-Tailed Data

arXiv 2208.09833 ↗First posted 21 Aug 2022cs.LGcited by 17

Not on the map. Why?

Abstract (arXiv)

Label noise is one of the key factors that lead to the poor generalization of deep learning models. Existing label-noise learning methods usually assume that the ground-truth classes of the training data are balanced. However, the real-world data is often imbalanced, leading to the inconsistency between observed and intrinsic class distribution with label noises. In this case, it is hard to distinguish clean samples from noisy samples on the intrinsic tail classes with the unknown intrinsic class distribution. In this paper, we propose a learning framework for label-noise learning with intrinsically long-tailed data. Specifically, we propose two-stage bi-dimensional sample selection (TABASCO) to better separate clean samples from noisy samples, especially for the tail classes. TABASCO consists of two new separation metrics that complement each other to compensate for the limitation of using a single metric in sample separation. Extensive experiments on benchmarks demonstrate the effectiveness of our method. Our code is available at https://github.com/Wakings/TABASCO.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 46 of its 63 references that are in the map. First posted on arXiv 21 Aug 2022.

Builds on

46 of 63 references.

References · 46

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  5. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  6. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  7. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,239
  8. 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,215
  9. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,053
  10. 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 987
  11. 2019Decoupling Representation and Classifier for Long-Tailed Recognitioncited by 806
  12. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
34 more
  1. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
  2. 2020Long-tail learning via logit adjustmentcited by 605
  3. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 580
  4. 2017Regularizing Neural Networks by Penalizing Confident Output Distributionscited by 461
  5. 2020Balanced Meta-Softmax for Long-Tailed Visual Recognitioncited by 397
  6. 2020Equalization Loss for Long-Tailed Object Recognitioncited by 285
  7. 2020Long-tailed Recognition by Routing Diverse Distribution-Aware Expertscited by 270
  8. 2021Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotationscited by 253
  9. 2018Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noisecited by 216
  10. 2021Improving Calibration for Long-Tailed Recognitioncited by 212
  11. 2020Does label smoothing mitigate label noise?cited by 204
  12. 2021Distribution Alignment: A Unified Framework for Long-tail Visual Recognitioncited by 198
  13. 2021CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learningcited by 174
  14. 2020Remix: Rebalanced Mixupcited by 144
  15. 2020Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learningcited by 142
  16. 2020A Survey of Label-noise Representation Learning: Past, Present and Futurecited by 121
  17. 2022Selective-Supervised Contrastive Learning with Noisy Labelscited by 112
  18. 2020Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learningcited by 105
  19. 2021Influence-Balanced Loss for Imbalanced Visual Classificationcited by 91
  20. 2017The Devil is in the Tails: Fine-grained Classification in the Wildcited by 89
  21. 2020Multi-Objective Interpolation Training for Robustness to Label Noisecited by 86
  22. 2021Jo-SRC: A Contrastive Approach for Combating Noisy Labelscited by 80
  23. 2021Sample Selection with Uncertainty of Losses for Learning with Noisy Labelscited by 79
  24. 2022UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learningcited by 77
  25. 2021FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentationcited by 76
  26. 2021Distilling Virtual Examples for Long-tailed Recognitioncited by 72
  27. 2021ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learningcited by 65
  28. 2020Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularizationcited by 49
  29. 2021Robust Long-Tailed Learning under Label Noisecited by 35
  30. 2022Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasetscited by 28
  31. 2021Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classificationcited by 19
  32. 2022Identifying Hard Noise in Long-Tailed Sample Distributioncited by 19
  33. 2021Learning From Long-Tailed Data With Noisy Labelscited by 17
  34. 2021Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Datacited by 16

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 7; 2025: 4; 2026: 4.

17 papers.

Sort:

  1. 2025TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detectioncited by 5
  2. 2025Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examplescited by 4
  3. 2024On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extractioncited by 3
  4. 2024Extracting Clean and Balanced Subset for Noisy Long-tailed Classificationcited by 3
  5. 2022Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Losscited by 2
  6. 2024Distribution-Aware Robust Learning from Long-Tailed Data with Noisy Labelscited by 2
  7. 2024Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmarkcited by 1
  8. 2025Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-tailed Noisy Datacited by 1
  9. 2026CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labelscited by 1
  10. 2023Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center Losscited by 0
  11. 2024Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Raritycited by 0
  12. 2024Learning from Noisy Labels for Long-tailed Data via Optimal Transportcited by 0
5 more
  1. 2024GBRIP: Granular Ball Representation for Imbalanced Partial Label Learningcited by 0
  2. 2025Soft causal learning for generalized molecule property prediction: An environment perspectivecited by 0
  3. 2026Learning from Imperfect Text Guidance: Robust Long-Tail Visual Recognition with High-Noise Labelcited by 0
  4. 2026Benchmarking Instance-Dependent Label Noise with Controlled Corruptionscited by 0
  5. 2026Neural Architecture Search of Sample Reweighting Networks for Complex Distribution Shiftcited by 0