% Key papers for Feature alignment for unsupervised adaptation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2019_contrastive_13972,
  title = {{Contrastive Adaptation Network for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2019_universal_14965,
  title = {{Universal Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2019_progressive_14673,
  title = {{Progressive Feature Alignment for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2018_deep_12957,
  title = {{Deep Cocktail Network: Multi-Source Unsupervised Domain Adaptation With Category Shift}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Xu_Deep_Cocktail_Network_CVPR_2018_paper.html},
}

@inproceedings{aaai2019_aligning_101836,
  title = {{Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4551},
}

@inproceedings{cvpr2020_unsupervised_16416,
  title = {{Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2018_unsupervised_13684,
  title = {{Unsupervised Domain Adaptation With Similarity Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Pinheiro_Unsupervised_Domain_Adaptation_CVPR_2018_paper.html},
}

@inproceedings{cvpr2021_fixbi_17027,
  title = {{FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2020_enhanced_15481,
  title = {{Enhanced Transport Distance for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2020_multi_15912,
  title = {{Multi-Modal Domain Adaptation for Fine-Grained Action Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2021_cross_16764,
  title = {{Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2020_stochastic_16286,
  title = {{Stochastic Classifiers for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2021_dynamic_16925,
  title = {{Dynamic Weighted Learning for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2021_prototypical_17673,
  title = {{Prototypical Cross-Domain Self-Supervised Learning for Few-Shot Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2018_re_13500,
  title = {{Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Re-Weighted_Adversarial_Adaptation_CVPR_2018_paper.html},
}

@inproceedings{cvpr2021_unsupervised_18091,
  title = {{Unsupervised Multi-Source Domain Adaptation Without Access to Source Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2022_category_18445,
  title = {{Category Contrast for Unsupervised Domain Adaptation in Visual Tasks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2020_reliable_16110,
  title = {{Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{aaai2021_a_16,
  title = {{A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08474-a-free-lunch-for-unsupervised-domain-adaptive-object-detection-without-source-data/},
}

@inproceedings{icml2020_do_38123,
  title = {{Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5793},
}

@inproceedings{aaai2024_prompt_7011,
  title = {{Prompt-Based Distribution Alignment for Unsupervised Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27830},
}

@inproceedings{cvpr2024_domain_23251,
  title = {{Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31368},
}

@inproceedings{cvpr2024_lead_23885,
  title = {{LEAD: Learning Decomposition for Source-free Universal Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31385},
}

@inproceedings{aaai2024_effective_5797,
  title = {{Effective Comparative Prototype Hashing for Unsupervised Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28674},
}

@inproceedings{aaai2024_on_6828,
  title = {{On Unsupervised Domain Adaptation: Pseudo Label Guided Mixup for Adversarial Prompt Tuning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29800},
}

@inproceedings{cvpr2025_preserving_27296,
  title = {{Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34783},
}

@inproceedings{aaai2025_dynamic_8853,
  title = {{Dynamic Target Distribution Estimation for Source-Free Open-Set Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34380},
}

@inproceedings{cvpr2024_universal_25146,
  title = {{Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30653},
}

@inproceedings{cvpr2024_unsupervised_25172,
  title = {{Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30341},
}

@inproceedings{neurips2024_enhancing_70195,
  title = {{Enhancing Domain Adaptation through Prompt Gradient Alignment}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96889},
}

@inproceedings{aaai2025_reducing_10491,
  title = {{Reducing Divergence in Batch Normalization for Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34369},
}

@inproceedings{cvpr2025_link_26802,
  title = {{Link-based Contrastive Learning for One-Shot Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32754},
}

@inproceedings{aaai2025_target_10926,
  title = {{Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33883},
}

@inproceedings{aaai2025_unsupervised_11183,
  title = {{Unsupervised Domain Adaptive Person Search via Dual Self-Calibration}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32702},
}

@inproceedings{aaai2025_gradient_9358,
  title = {{Gradient-Based Sample Selection for Black-Box Universal Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33881},
}

@inproceedings{neurips2024_revisiting_72787,
  title = {{Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97628},
}

@inproceedings{aaai2025_casual_8337,
  title = {{CASUAL: Conditional Support Alignment for Domain Adaptation with Label Shift}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34166},
}

@inproceedings{aaai2025_simprof_10762,
  title = {{SimProF: A Simple Probabilistic Framework for Unsupervised Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35413},
}

@inproceedings{cvpr2025_unsupervised_28026,
  title = {{Unsupervised Continual Domain Shift Learning with Multi-Prototype Modeling}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34275},
}

@inproceedings{iclr2024_can_34907,
  title = {{Can We Evaluate Domain Adaptation Models Without Target-Domain Labels?}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18167},
}