% Key papers for Noise-aware loss and pairwise methods (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2018_iterative_13238,
  title = {{Iterative Learning With Open-Set Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Iterative_Learning_With_CVPR_2018_paper.html},
}

@inproceedings{cvpr2019_learning_14395,
  title = {{Learning From Noisy Labels by Regularized Estimation of Annotator Confusion}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2021_meta_982,
  title = {{Meta Label Correction for Noisy Label Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/11053-meta-label-correction-for-noisy-label-learning/},
}

@inproceedings{cvpr2022_unicon_20123,
  title = {{UniCon: Combating Label Noise Through Uniform Selection and Contrastive Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2018_masking_57358,
  title = {{Masking: A New Perspective of Noisy Supervision}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11568},
}

@inproceedings{icml2020_peer_38616,
  title = {{Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6576},
}

@inproceedings{iclr2022_learning_31310,
  title = {{Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/7085},
}

@inproceedings{cvpr2023_fine_20999,
  title = {{Fine-Grained Classification With Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/20943},
}

@inproceedings{cvpr2022_few_18852,
  title = {{Few-Shot Learning With Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{iclr2022_sample_31834,
  title = {{Sample Selection with Uncertainty of Losses for Learning with Noisy Labels}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/7175},
}

@inproceedings{cvpr2022_scalable_19792,
  title = {{Scalable Penalized Regression for Noise Detection in Learning With Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{iclr2021_robust_30602,
  title = {{Robust Curriculum Learning: from clean label detection to noisy label self-correction}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3000},
}

@inproceedings{cvpr2021_dualgraph_16912,
  title = {{DualGraph: A Graph-Based Method for Reasoning About Label Noise}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{aaai2020_are_103097,
  title = {{Are Noisy Sentences Useless for Distant Supervised Relation Extraction?}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6407},
}

@inproceedings{icml2020_error_38168,
  title = {{Error-Bounded Correction of Noisy Labels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6161},
}

@inproceedings{cvpr2019_weakly_15018,
  title = {{Weakly Supervised Image Classification Through Noise Regularization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2021_open_63301,
  title = {{Open-set Label Noise Can Improve Robustness Against Inherent Label Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/37791},
}

@inproceedings{aaai2021_robustness_1297,
  title = {{Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/11451-robustness-of-accuracy-metric-and-its-inspirations-in-learning-with-noisy-labels/},
}

@inproceedings{neurips2021_generalized_62309,
  title = {{Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27006},
}

@inproceedings{icml2021_clusterability_39137,
  title = {{Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10625},
}

@inproceedings{cvpr2024_learning_23954,
  title = {{Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31228},
}

@inproceedings{aaai2024_learning_6514,
  title = {{Learning with Noisy Labels Using Hyperspherical Margin Weighting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29626},
}

@inproceedings{aaai2024_regroup_7095,
  title = {{Regroup Median Loss for Combating Label Noise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29250},
}

@inproceedings{aaai2025_optimized_10217,
  title = {{Optimized Gradient Clipping for Noisy Label Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33025},
}

@inproceedings{aaai2025_enhanced_8947,
  title = {{Enhanced Sample Selection with Confidence Tracking: Identifying Correctly Labeled Yet Hard-to-Learn Samples in Noisy Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34180},
}

@inproceedings{aaai2025_noisy_10126,
  title = {{Noisy Label Calibration for Multi-View Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35485},
}

@inproceedings{aaai2025_revisiting_10554,
  title = {{Revisiting Interpolation for Noisy Label Correction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35489},
}

@inproceedings{aaai2025_robust_10574,
  title = {{Robust Logit Adjustment for Learning with Long-Tailed Noisy Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33738},
}

@inproceedings{neurips2024_noisegpt_72460,
  title = {{NoiseGPT: Label Noise Detection and Rectification through Probability Curvature}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94898},
}

@inproceedings{aaai2025_learning_9766,
  title = {{Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32673},
}

@inproceedings{aaai2025_learning_9758,
  title = {{Learning from Noisy Labels via Self-Taught On-the-Fly Meta Loss Rescaling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34580},
}

@inproceedings{aaai2026_is_107011,
  title = {{Is the Information Bottleneck Robust Enough? Towards Label-Noise Resistant Information Bottleneck Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39363},
}

@inproceedings{neurips2024_sample_71351,
  title = {{Sample Selection via Contrastive Fragmentation for Noisy Label Regression}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95898},
}

@inproceedings{neurips2024_information_71624,
  title = {{Information-theoretic Limits of Online Classification with Noisy Labels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95650},
}

@inproceedings{neurips2024_cosw_72169,
  title = {{CoSW: Conditional Sample Weighting for Smoke Segmentation with Label Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95170},
}

@inproceedings{neurips2025_enhancing_76929,
  title = {{Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=OfIUAlo2hJ},
}

@inproceedings{neurips2025_noisy_80446,
  title = {{Noisy Multi-Label Learning through Co-Occurrence-Aware Diffusion}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=zft0zTOFkN},
}

@inproceedings{iclr2026_noise_89239,
  title = {{Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=wb83wO41QT},
}

@inproceedings{aaai2026_prototype_107125,
  title = {{Prototype-Guided Supervision for Graph Learning with Noisy and Sparse Labels}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39477},
}

@inproceedings{icml2024_don_45834,
  title = {{Don’t Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32618},
}