% Key papers for Noisy cross-modal hashing and multi-label (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_synthetic_28424,
  title = {{Synthetic and Natural Noise Both Break Neural Machine Translation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/172},
}

@inproceedings{cvpr2022_learning_19238,
  title = {{Learning With Twin Noisy Labels for Visible-Infrared Person Re-Identification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2016_seeing_11843,
  title = {{Seeing Through the Human Reporting Bias: Visual Classifiers From Noisy Human-Centric Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{cvpr2019_metacleaner_14514,
  title = {{MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2017_attend_12069,
  title = {{Attend in Groups: A Weakly-Supervised Deep Learning Framework for Learning From Web Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2022_large_19160,
  title = {{Large Loss Matters in Weakly Supervised Multi-Label Classification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2022_large_19161,
  title = {{Large-Scale Pre-Training for Person Re-Identification With Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2021_faster_17011,
  title = {{Faster Meta Update Strategy for Noise-Robust Deep Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2021_proselflc_17650,
  title = {{ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{aaai2019_crash_100179,
  title = {{Crash to Not Crash: Learn to Identify Dangerous Vehicles Using a Simulator}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3887},
}

@inproceedings{cvpr2023_noisy_21712,
  title = {{Noisy Correspondence Learning With Meta Similarity Correction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21875},
}

@inproceedings{iclr2021_mopro_29938,
  title = {{MoPro: Webly Supervised Learning with Momentum Prototypes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2567},
}

@inproceedings{cvpr2022_mutual_19406,
  title = {{Mutual Quantization for Cross-Modal Search With Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2016_noisy_11729,
  title = {{Noisy Label Recovery for Shadow Detection in Unfamiliar Domains}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{aaai2023_admoe_3039,
  title = {{ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25620},
}

@inproceedings{aaai2020_coupled_102036,
  title = {{Coupled-View Deep Classifier Learning from Multiple Noisy Annotators}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5898},
}

@inproceedings{iclr2020_learning_29673,
  title = {{Learning from Rules Generalizing Labeled Exemplars}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SkeuexBtDr},
}

@inproceedings{aaai2025_robust_10578,
  title = {{Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34199},
}

@inproceedings{aaai2020_a_101921,
  title = {{A Skip-Connected Evolving Recurrent Neural Network for Data Stream Classification under Label Latency Scenario}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5781},
}

@inproceedings{cvpr2025_roll_27366,
  title = {{ROLL: Robust Noisy Pseudo-label Learning for Multi-View Clustering with Noisy Correspondence}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34784},
}

@inproceedings{aaai2024_noisy_6784,
  title = {{Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27911},
}

@inproceedings{aaai2025_calibrated_8375,
  title = {{Calibrated Disambiguation for Partial Multi-label Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34049},
}

@inproceedings{aaai2024_learning_6462,
  title = {{Learning MDL Logic Programs from Noisy Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28925},
}

@inproceedings{aaai2025_distribution_8770,
  title = {{Distribution-Consistency-Guided Multi-modal Hashing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33326},
}

@inproceedings{neurips2024_collaborative_72167,
  title = {{Collaborative Refining for Learning from Inaccurate Labels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95171},
}

@inproceedings{aaai2024_colal_5408,
  title = {{CoLAL: Co-learning Active Learning for Text Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29235},
}

@inproceedings{cvpr2025_din_25924,
  title = {{DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33454},
}

@inproceedings{neurips2024_noisy_71775,
  title = {{Noisy Ostracods: A Fine-Grained, Imbalanced Real-World Dataset for Benchmarking Robust Machine Learning and Label Correction Methods}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97733},
}

@inproceedings{neurips2024_dopanim_72606,
  title = {{dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97647},
}

@inproceedings{neurips2025_an_79699,
  title = {{An Analysis of Concept Bottleneck Models: Measuring, Understanding, and Mitigating the Impact of Noisy Annotations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=s3TvewYDKk},
}

@inproceedings{aaai2026_neighbor_107215,
  title = {{Neighbor-aware Instance Refining with Noisy Labels for Cross-Modal Retrieval}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39567},
}

@inproceedings{icml2026_care_50486,
  title = {{CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Csmrtng6PP},
}

@inproceedings{iclr2025_gift_81447,
  title = {{GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30327},
}

@inproceedings{neurips2026_an_90492,
  title = {{An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/139664},
}

@inproceedings{neurips2026_unveiling_90936,
  title = {{Unveiling Memorization–Generalization Coexistence: A Case Study on Arithmetic Tasks with Label Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/148534},
}

@inproceedings{aaai2026_cognitrust_100691,
  title = {{CogniTrust: Cognitive Memory-Driven Verifiable Supervision for Robust Hashing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/42431},
}

@inproceedings{aaai2026_hana_106382,
  title = {{HaNa: Hardness and Noise-Aware Robust Cross-modal Retrieval}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38689},
}

@inproceedings{aaai2026_pcsr_107226,
  title = {{PCSR: Pseudo-label Consistency-Guided Sample Refinement for Noisy Correspondence Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39578},
}