% Key papers for Deep active learning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_active_28493,
  title = {{Active Learning for Convolutional Neural Networks: A Core-Set Approach}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/194},
}

@inproceedings{cvpr2018_the_13647,
  title = {{The Power of Ensembles for Active Learning in Image Classification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Beluch_The_Power_of_CVPR_2018_paper.html},
}

@inproceedings{cvpr2021_task_17979,
  title = {{Task-Aware Variational Adversarial Active Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2020_state_16281,
  title = {{State-Relabeling Adversarial Active Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2022_active_18252,
  title = {{Active Learning by Feature Mixing}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{aaai2019_self_101730,
  title = {{Self-Paced Active Learning: Query the Right Thing at the Right Time}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4445},
}

@inproceedings{iclr2017_predicting_28359,
  title = {{Predicting Medications from Diagnostic Codes with Recurrent Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=rJEgeXFex},
}

@inproceedings{iclr2019_active_28905,
  title = {{Active Learning with Partial Feedback}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/794},
}

@inproceedings{cvpr2021_vab_18110,
  title = {{VaB-AL: Incorporating Class Imbalance and Difficulty With Variational Bayes for Active Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2022_active_18253,
  title = {{Active Learning for Open-Set Annotation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2018_joint_57255,
  title = {{Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11153},
}

@inproceedings{iclr2021_on_30185,
  title = {{On Statistical Bias In Active Learning: How and When to Fix It}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3037},
}

@inproceedings{cvpr2023_active_20343,
  title = {{Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22123},
}

@inproceedings{aaai2022_boosting_1794,
  title = {{Boosting Active Learning via Improving Test Performance}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/08566-boosting-active-learning-via-improving-test-performance/},
}

@inproceedings{aaai2020_active_102271,
  title = {{Active Learning with Query Generation for Cost-Effective Text Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6133},
}

@inproceedings{aaai2024_entropic_5867,
  title = {{Entropic Open-Set Active Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28269},
}

@inproceedings{aaai2022_similarity_2750,
  title = {{Similarity Search for Efficient Active Learning and Search of Rare Concepts}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/06402-similarity-search-for-efficient-active-learning-and-search-of-rare-concepts/},
}

@inproceedings{neurips2021_similar_63532,
  title = {{SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26574},
}

@inproceedings{icml2019_bayesian_37180,
  title = {{Bayesian Generative Active Deep Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3619},
}

@inproceedings{aaai2019_active_101646,
  title = {{Active Sampling for Open-Set Classification without Initial Annotation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4353},
}

@inproceedings{cvpr2024_evidential_23400,
  title = {{Evidential Active Recognition: Intelligent and Prudent Open-World Embodied Perception}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30912},
}

@inproceedings{cvpr2024_activedc_22713,
  title = {{ActiveDC: Distribution Calibration for Active Finetuning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30362},
}

@inproceedings{aaai2024_composite_5446,
  title = {{Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29119},
}

@inproceedings{cvpr2025_rethinking_27442,
  title = {{Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33978},
}

@inproceedings{neurips2024_boundary_70405,
  title = {{Boundary Matters: A Bi-Level Active Finetuning Method}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96700},
}

@inproceedings{aaai2024_inconsistency_6312,
  title = {{Inconsistency-Based Data-Centric Active Open-Set Annotation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28213},
}

@inproceedings{cvpr2025_instance_26631,
  title = {{Instance-wise Supervision-level Optimization in Active Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33908},
}

@inproceedings{neurips2024_a_71928,
  title = {{A Cross-Domain Benchmark for Active Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97719},
}

@inproceedings{aaai2024_combining_5430,
  title = {{Combining Graph Transformers Based Multi-Label Active Learning and Informative Data Augmentation for Chest Xray Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30133},
}

@inproceedings{icml2024_provably_45220,
  title = {{Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33232},
}

@inproceedings{icml2026_learning_52881,
  title = {{Learning-To-Measure: In-Context Active Feature Acquisition}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=ZumBSRsRbE},
}

@inproceedings{neurips2024_contextual_72779,
  title = {{Contextual Active Model Selection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94616},
}

@inproceedings{neurips2024_sel_74199,
  title = {{SEL-BALD: Deep Bayesian Active Learning with Selective Labels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93351},
}

@inproceedings{neurips2025_formal_75644,
  title = {{Formal Models of Active Learning from Contrastive Examples}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=AQ21krZgax},
}

@inproceedings{neurips2025_enhancing_78761,
  title = {{Enhancing Deep Batch Active Learning for Regression with Imperfect Data Guided Selection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=i5rApSWC9E},
}

@inproceedings{iclr2025_uncertainty_82328,
  title = {{Uncertainty Herding: One Active Learning Method for All Label Budgets}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29463},
}

@inproceedings{neurips2026_prediction_94330,
  title = {{Prediction-Powered Active Testing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155200},
}

@inproceedings{cvpr2026_revisiting_95974,
  title = {{Revisiting Unknowns: Towards Effective and Efficient Open-Set Active Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Zong_Revisiting_Unknowns_Towards_Effective_and_Efficient_Open-Set_Active_Learning_CVPR_2026_paper.html},
}

@inproceedings{cvpr2026_cleaning_96402,
  title = {{Cleaning the Pool: Progressive Filtering of Unlabeled Pools in Deep Active Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Huseljic_Cleaning_the_Pool_Progressive_Filtering_of_Unlabeled_Pools_in_Deep_CVPR_2026_paper.html},
}