% Key papers for Realistic and stable test-time adaptation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2021_tent_30710,
  title = {{Tent: Fully Test-Time Adaptation by Entropy Minimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2874},
}

@inproceedings{cvpr2022_contrastive_18526,
  title = {{Contrastive Test-Time Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2021_test_62781,
  title = {{Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27675},
}

@inproceedings{cvpr2023_back_20441,
  title = {{Back to the Source: Diffusion-Driven Adaptation To Test-Time Corruption}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21631},
}

@inproceedings{cvpr2023_improved_21227,
  title = {{Improved Test-Time Adaptation for Domain Generalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21923},
}

@inproceedings{neurips2020_learning_60180,
  title = {{Learning Loss for Test-Time Augmentation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16933},
}

@inproceedings{neurips2022_test_64965,
  title = {{Test-Time Training with Masked Autoencoders}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54688},
}

@inproceedings{cvpr2023_tesla_22312,
  title = {{TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21855},
}

@inproceedings{cvpr2023_tipi_22299,
  title = {{TIPI: Test Time Adaptation With Transformation Invariance}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21206},
}

@inproceedings{aaai2024_test_7524,
  title = {{Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29527},
}

@inproceedings{iclr2023_ttn_32275,
  title = {{TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11100},
}

@inproceedings{aaai2024_towards_7613,
  title = {{Towards Real-World Test-Time Adaptation: Tri-net Self-Training with Balanced Normalization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29435},
}

@inproceedings{cvpr2023_actmad_20339,
  title = {{ActMAD: Activation Matching To Align Distributions for Test-Time-Training}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/23197},
}

@inproceedings{cvpr2023_neuro_21705,
  title = {{Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21215},
}

@inproceedings{icml2023_adanpc_41478,
  title = {{AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24782},
}

@inproceedings{cvpr2024_test_24973,
  title = {{Test-Time Adaptation for Depth Completion}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31657},
}

@inproceedings{cvpr2023_train_22402,
  title = {{Train/Test-Time Adaptation With Retrieval}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21250},
}

@inproceedings{cvpr2024_backpropagation_22816,
  title = {{Backpropagation-free Network for 3D Test-time Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30152},
}

@inproceedings{iclr2023_delta_32917,
  title = {{DELTA: DEGRADATION-FREE FULLY TEST-TIME ADAPTATION}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/12204},
}

@inproceedings{neurips2024_cross_72729,
  title = {{Cross-Device Collaborative Test-Time Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94660},
}

@inproceedings{aaai2025_a_7899,
  title = {{A Layer Selection Approach to Test Time Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34229},
}

@inproceedings{aaai2025_attention_8169,
  title = {{Attention Bootstrapping for Multi-Modal Test-Time Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34446},
}

@inproceedings{aaai2025_fully_9254,
  title = {{Fully Test-time Adaptation for Tabular Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34466},
}

@inproceedings{aaai2025_learning_9761,
  title = {{Learning to Generate Gradients for Test-Time Adaptation via Test-Time Training Layers}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33783},
}

@inproceedings{neurips2024_protected_74038,
  title = {{Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93486},
}

@inproceedings{aaai2024_unraveling_7710,
  title = {{Unraveling Batch Normalization for Realistic Test-Time Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29436},
}

@inproceedings{cvpr2025_effortless_26107,
  title = {{Effortless Active Labeling for Long-Term Test-Time Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34584},
}

@inproceedings{cvpr2025_test_27845,
  title = {{Test-time Augmentation Improves Efficiency in Conformal Prediction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34685},
}

@inproceedings{neurips2024_test_70135,
  title = {{Test-time Adaptation in Non-stationary Environments via Adaptive Representation Alignment}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96943},
}

@inproceedings{cvpr2025_tide_27810,
  title = {{TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33424},
}

@inproceedings{iclr2024_active_34646,
  title = {{Active Test-Time Adaptation: Theoretical Analyses and An Algorithm}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18428},
}

@inproceedings{neurips2024_test_73317,
  title = {{Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94128},
}

@inproceedings{iclr2024_un_35642,
  title = {{Un-Mixing Test-Time Normalization Statistics: Combatting Label Temporal Correlation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17431},
}

@inproceedings{icml2025_it_47034,
  title = {{IT\$^3\$: Idempotent Test-Time Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45551},
}

@inproceedings{icml2025_beyond_47281,
  title = {{Beyond Entropy: Region Confidence Proxy for Wild Test-Time Adaptation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45308},
}