% Key papers for Instance-dependent noise modeling (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2019_graph_14258,
  title = {{Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2018_cleannet_12902,
  title = {{CleanNet: Transfer Learning for Scalable Image Classifier Training With Label Noise}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Lee_CleanNet_Transfer_Learning_CVPR_2018_paper.html},
}

@inproceedings{icml2019_unsupervised_37841,
  title = {{Unsupervised Label Noise Modeling and Loss Correction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3738},
}

@inproceedings{neurips2019_are_57908,
  title = {{Are Anchor Points Really Indispensable in Label-Noise Learning?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13788},
}

@inproceedings{cvpr2020_distilling_15422,
  title = {{Distilling Effective Supervision From Severe Label Noise}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2020_part_60570,
  title = {{Part-dependent Label Noise: Towards Instance-dependent Label Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17030},
}

@inproceedings{neurips2018_using_57757,
  title = {{Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11989},
}

@inproceedings{aaai2021_beyond_195,
  title = {{Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/11442-beyond-class-conditional-assumption-a-primary-attempt-to-combat-instance-dependent-label-noise/},
}

@inproceedings{cvpr2021_re_17701,
  title = {{Re-Labeling ImageNet: From Single to Multi-Labels, From Global to Localized Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2020_dual_59742,
  title = {{Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17012},
}

@inproceedings{icml2019_combating_37228,
  title = {{Combating Label Noise in Deep Learning using Abstention}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4141},
}

@inproceedings{neurips2021_pervasive_62450,
  title = {{Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://openreview.net/forum?id=XccDXrDNLek},
}

@inproceedings{cvpr2021_a_16544,
  title = {{A Second-Order Approach to Learning With Instance-Dependent Label Noise}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2016_estimating_55714,
  title = {{Estimating the class prior and posterior from noisy positives and unlabeled data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6938},
}

@inproceedings{cvpr2020_global_15609,
  title = {{Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2022_instance_19085,
  title = {{Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix Estimation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{aaai2021_label_850,
  title = {{Label Confusion Learning to Enhance Text Classification Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/12929-label-confusion-learning-to-enhance-text-classification-models/},
}

@inproceedings{aaai2019_safeguarded_103544,
  title = {{Safeguarded Dynamic Label Regression for Noisy Supervision}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4943},
}

@inproceedings{aaai2022_uncertainty_2903,
  title = {{Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/06960-uncertainty-aware-learning-against-label-noise-on-imbalanced-datasets/},
}

@inproceedings{icml2016_loss_35894,
  title = {{Loss factorization, weakly supervised learning and label noise robustness}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/patrini16.html},
}

@inproceedings{aaai2024_adaptive_5132,
  title = {{Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28284},
}

@inproceedings{aaai2024_toward_7576,
  title = {{Toward Robustness in Multi-Label Classification: A Data Augmentation Strategy against Imbalance and Noise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30157},
}

@inproceedings{cvpr2024_estimating_23389,
  title = {{Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31779},
}

@inproceedings{aaai2024_unlocking_7708,
  title = {{Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29469},
}

@inproceedings{aaai2024_which_7824,
  title = {{Which Is More Effective in Label Noise Cleaning, Correction or Filtering?}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29183},
}

@inproceedings{cvpr2024_crosel_23058,
  title = {{CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30997},
}

@inproceedings{aaai2024_mitigating_6648,
  title = {{Mitigating Label Noise through Data Ambiguation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29286},
}

@inproceedings{aaai2025_rethinking_10538,
  title = {{Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33094},
}

@inproceedings{neurips2024_imprecise_70148,
  title = {{Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96933},
}

@inproceedings{cvpr2025_theory_27866,
  title = {{Theory-Inspired Deep Multi-View Multi-Label Learning with Incomplete Views and Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34682},
}

@inproceedings{aaai2024_limited_6534,
  title = {{Limited-Supervised Multi-Label Learning with Dependency Noise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29494},
}

@inproceedings{iclr2024_understanding_34519,
  title = {{Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18555},
}

@inproceedings{aaai2025_label_9694,
  title = {{Label Noise Correction via Fuzzy Learning Machine}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34055},
}

@inproceedings{aaai2025_learning_9723,
  title = {{Learning Causal Transition Matrix for Instance-dependent Label Noise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34014},
}

@inproceedings{aaai2025_repface_10513,
  title = {{RepFace: Refining Closed-Set Noise with Progressive Label Correction for Face Recognition}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33077},
}

@inproceedings{neurips2024_noisy_71422,
  title = {{Noisy Label Learning with Instance-Dependent Outliers: Identifiability via Crowd Wisdom}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95831},
}

@inproceedings{aaai2025_pre_10339,
  title = {{Pre-Trained Vision-Language Models as Noisy Partial Annotators}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35417},
}

@inproceedings{iclr2024_why_33571,
  title = {{Why is SAM Robust to Label Noise?}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19504},
}

@inproceedings{iclr2024_dirichlet_33801,
  title = {{Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19274},
}

@inproceedings{icml2024_learning_45796,
  title = {{Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32656},
}