% Key papers for Source-free domain adaptation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2016_return_105134,
  title = {{Return of Frustratingly Easy Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10306},
}

@inproceedings{neurips2016_domain_55688,
  title = {{Domain Separation Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7175},
}

@inproceedings{aaai2018_wasserstein_102517,
  title = {{Wasserstein Distance Guided Representation Learning for Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11784},
}

@inproceedings{cvpr2019_separate_14814,
  title = {{Separate to Adapt: Open Set Domain Adaptation via Progressive Separation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2020_universal_16408,
  title = {{Universal Source-Free Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{aaai2020_multi_103890,
  title = {{Multi-Source Distilling Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6997},
}

@inproceedings{icml2016_domain_35802,
  title = {{Domain Adaptation with Conditional Transferable Components}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/gong16.html},
}

@inproceedings{cvpr2016_learning_11654,
  title = {{Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{cvpr2021_domain_16897,
  title = {{Domain Adaptation With Auxiliary Target Domain-Oriented Classifier}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2018_boosting_12874,
  title = {{Boosting Domain Adaptation by Discovering Latent Domains}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Mancini_Boosting_Domain_Adaptation_CVPR_2018_paper.html},
}

@inproceedings{cvpr2022_source_19903,
  title = {{Source-Free Domain Adaptation via Distribution Estimation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2021_cross_16763,
  title = {{Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2021_transferable_18044,
  title = {{Transferable Semantic Augmentation for Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2021_exploiting_63402,
  title = {{Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28283},
}

@inproceedings{cvpr2022_exploring_18797,
  title = {{Exploring Domain-Invariant Parameters for Source Free Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2023_c_20519,
  title = {{C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21251},
}

@inproceedings{cvpr2021_learning_17283,
  title = {{Learning Invariant Representations and Risks for Semi-Supervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{icml2019_domain_37310,
  title = {{Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3752},
}

@inproceedings{aaai2020_multi_101270,
  title = {{Multi-Source Domain Adaptation for Visual Sentiment Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5651},
}

@inproceedings{cvpr2021_regressive_17726,
  title = {{Regressive Domain Adaptation for Unsupervised Keypoint Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2024_source_24856,
  title = {{Source-Free Domain Adaptation with Frozen Multimodal Foundation Model}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30212},
}

@inproceedings{cvpr2024_revisiting_24658,
  title = {{Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30117},
}

@inproceedings{cvpr2024_understanding_25121,
  title = {{Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29853},
}

@inproceedings{aaai2024_ufda_7668,
  title = {{UFDA: Universal Federated Domain Adaptation with Practical Assumptions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29311},
}

@inproceedings{aaai2024_open_6856,
  title = {{Open-Set Graph Domain Adaptation via Separate Domain Alignment}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28765},
}

@inproceedings{neurips2024_style_71642,
  title = {{Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95635},
}

@inproceedings{cvpr2024_discriminative_23226,
  title = {{Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30820},
}

@inproceedings{aaai2025_collaborative_8455,
  title = {{Collaborative Semantic Consistency Alignment for Blended-Target Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34110},
}

@inproceedings{iclr2024_sf_35100,
  title = {{SF(DA)\$^2\$: Source-free Domain Adaptation Through the Lens of Data Augmentation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17973},
}

@inproceedings{cvpr2025_revisiting_27464,
  title = {{Revisiting Source-Free Domain Adaptation: Insights into Representativeness, Generalization, and Variety}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32745},
}

@inproceedings{aaai2024_target_7496,
  title = {{Target-Free Domain Adaptation through Cross-Adaptation (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30490},
}

@inproceedings{aaai2025_invertible_9601,
  title = {{Invertible Projection and Conditional Alignment for Multi-Source Blended-Target Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34111},
}

@inproceedings{aaai2025_open_10202,
  title = {{Open-Set Heterogeneous Domain Adaptation: Theoretical Analysis and Algorithm}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34191},
}

@inproceedings{cvpr2025_adu_25390,
  title = {{ADU: Adaptive Detection of Unknown Categories in Black-Box Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33972},
}

@inproceedings{iclr2025_proxy_81424,
  title = {{Proxy Denoising for Source-Free Domain Adaptation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30349},
}

@inproceedings{icml2025_theoretical_47675,
  title = {{Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44922},
}

@inproceedings{icml2025_cosda_48907,
  title = {{COSDA: Counterfactual-based Susceptibility Risk Framework for Open-Set Domain Adaptation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43716},
}

@inproceedings{icml2026_rethinking_53804,
  title = {{Rethinking the Flow-based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspective}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=iqXzDUd36x},
}

@inproceedings{neurips2025_duet_74711,
  title = {{DUET: Dual-Perspective Pseudo Labeling and Uncertainty-aware Exploration \& Exploitation Training for Source-Free Domain Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=0M2M2EVreG},
}

@inproceedings{neurips2025_diffusion_77959,
  title = {{Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Zb0FzAjWN4},
}