mlsanityData to 14 Oct 2026

Boosting Co-teaching with Compression Regularization for Label Noise

arXiv 2104.13766 ↗First posted 28 Apr 2021cs.CVcited by 31

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Abstract (arXiv)

In this paper, we study the problem of learning image classification models in the presence of label noise. We revisit a simple compression regularization named Nested Dropout. We find that Nested Dropout, though originally proposed to perform fast information retrieval and adaptive data compression, can properly regularize a neural network to combat label noise. Moreover, owing to its simplicity, it can be easily combined with Co-teaching to further boost the performance. Our final model remains simple yet effective: it achieves comparable or even better performance than the state-of-the-art approaches on two real-world datasets with label noise which are Clothing1M and ANIMAL-10N. On Clothing1M, our approach obtains 74.9% accuracy which is slightly better than that of DivideMix. On ANIMAL-10N, we achieve 84.1% accuracy while the best public result by PLC is 83.4%. We hope that our simple approach can be served as a strong baseline for learning with label noise. Our implementation is available at https://github.com/yingyichen-cyy/Nested-Co-teaching.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 28 Apr 2021.

Builds on

20 of 30 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  4. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,059
  5. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 692
  6. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 665
  7. 2017MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labelscited by 653
  8. 2018Exploring the Limits of Weakly Supervised Pretrainingcited by 605
  9. 2019How does Disagreement Help Generalization against Label Corruption?cited by 390
  10. 2018Joint Optimization Framework for Learning with Noisy Labelscited by 293
  11. 2020Combating noisy labels by agreement: A joint training method with co-regularizationcited by 289
  12. 2017WebVision Database: Visual Learning and Understanding from Web Datacited by 262
8 more
  1. 2017Decoupling "when to update" from "how to update"cited by 230
  2. 2019Probabilistic End-to-end Noise Correction for Learning with Noisy Labelscited by 210
  3. 2018Dimensionality-Driven Learning with Noisy Labelscited by 173
  4. 2018Learning to Learn from Noisy Labeled Datacited by 160
  5. 2021Learning with Feature-Dependent Label Noise: A Progressive Approachcited by 102
  6. 2014Learning Ordered Representations with Nested Dropoutcited by 84
  7. 2019Learning Sparse Networks Using Targeted Dropoutcited by 40
  8. 2018A Semi-Supervised Two-Stage Approach to Learning from Noisy Labelscited by 29

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 14; 2023: 6; 2024: 5; 2025: 2; 2026: 1.

31 papers.

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  1. 2022ProMix: Combating Label Noise via Maximizing Clean Sample Utilitycited by 48
  2. 2024Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discoverycited by 31
  3. 2022SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labelscited by 24
  4. 2022Neighborhood Collective Estimation for Noisy Label Identification and Correctioncited by 22
  5. 2022Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformercited by 21
  6. 2022Identifying Hard Noise in Long-Tailed Sample Distributioncited by 19
  7. 2021Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noisecited by 18
  8. 2022Instance-Dependent Noisy Label Learning via Graphical Modellingcited by 17
  9. 2024CLIPCleaner: Cleaning Noisy Labels with CLIPcited by 16
  10. 2022Scalable Penalized Regression for Noise Detection in Learning with Noisy Labelscited by 15
  11. 2021SSR: An Efficient and Robust Framework for Learning with Unknown Label Noisecited by 13
  12. 2022L2B: Learning to Bootstrap Robust Models for Combating Label Noisecited by 10
19 more
  1. 2023Learning with Noisy labels via Self-supervised Adversarial Noisy Maskingcited by 10
  2. 2022Unsupervised Learning under Latent Label Shiftcited by 9
  3. 2022Compressing Features for Learning with Noisy Labelscited by 7
  4. 2022ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy Statecited by 5
  5. 2022Learning with Noisy Labels over Imbalanced Subpopulationscited by 5
  6. 2021CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learningcited by 3
  7. 2024ANNE: Adaptive Nearest Neighbors and Eigenvector-based Sample Selection for Robust Learning with Noisy Labelscited by 3
  8. 2022CNTN: Cyclic Noise-tolerant Network for Gait Recognitioncited by 2
  9. 2022Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations (Full Version)cited by 1
  10. 2023Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learningcited by 1
  11. 2023Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Modelling Approachcited by 1
  12. 2023PASS: Peer-Agreement based Sample Selection for training with Noisy Labelscited by 1
  13. 2023Bridging Generative and Discriminative Noisy-Label Learning via Direction-Agnostic EM Formulationcited by 1
  14. 2023Feature Noise Boosts DNN Generalization under Label Noisecited by 0
  15. 2024Collaboration of Teachers for Semi-supervised Object Detectioncited by 0
  16. 2024A Multi-module Robust Method for Transient Stability Assessment against False Label Injection Cyberattackscited by 0
  17. 2025CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labelscited by 0
  18. 2025Learning to Clean: Reinforcement Learning for Noisy Label Correctioncited by 0
  19. 2026Variational Rectification Inference for Learning with Noisy Labelscited by 0