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

@inproceedings{cvpr2017_adversarial_12048,
  title = {{Adversarial Discriminative Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2018_maximum_13362,
  title = {{Maximum Classifier Discrepancy 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/Saito_Maximum_Classifier_Discrepancy_CVPR_2018_paper.html},
}

@inproceedings{cvpr2017_deep_12151,
  title = {{Deep Hashing Network for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2017_unsupervised_12723,
  title = {{Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{neurips2018_conditional_56958,
  title = {{Conditional Adversarial Domain Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11178},
}

@inproceedings{aaai2018_multi_102500,
  title = {{Multi-Adversarial Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11767},
}

@inproceedings{cvpr2018_generate_13145,
  title = {{Generate to Adapt: Aligning Domains Using Generative Adversarial Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Sankaranarayanan_Generate_to_Adapt_CVPR_2018_paper.html},
}

@inproceedings{cvpr2018_image_13211,
  title = {{Image to Image Translation for Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Murez_Image_to_Image_CVPR_2018_paper.html},
}

@inproceedings{cvpr2018_collaborative_12907,
  title = {{Collaborative and Adversarial 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/Zhang_Collaborative_and_Adversarial_CVPR_2018_paper.html},
}

@inproceedings{cvpr2018_importance_13213,
  title = {{Importance Weighted Adversarial Nets for Partial Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Importance_Weighted_Adversarial_CVPR_2018_paper.html},
}

@inproceedings{cvpr2019_domain_14107,
  title = {{Domain-Specific Batch Normalization for Unsupervised Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{iclr2018_a_28499,
  title = {{A DIRT-T Approach to Unsupervised Domain Adaptation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/26},
}

@inproceedings{aaai2020_adversarial_102261,
  title = {{Adversarial Domain Adaptation with Domain Mixup}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6123},
}

@inproceedings{aaai2020_deep_103896,
  title = {{Deep Domain-Adversarial Image Generation for Domain Generalisation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/7003},
}

@inproceedings{cvpr2021_reducing_17719,
  title = {{Reducing Domain Gap by Reducing Style Bias}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2019_domain_14108,
  title = {{Domain-Symmetric Networks for Adversarial Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{icml2019_bridging_37201,
  title = {{Bridging Theory and Algorithm for Domain Adaptation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3630},
}

@inproceedings{neurips2018_adversarial_56846,
  title = {{Adversarial Multiple Source Domain Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11818},
}

@inproceedings{aaai2019_transferable_101757,
  title = {{Transferable Attention for Domain Adaptation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4472},
}

@inproceedings{cvpr2018_adversarial_12826,
  title = {{Adversarial Feature Augmentation 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/Volpi_Adversarial_Feature_Augmentation_CVPR_2018_paper.html},
}

@inproceedings{cvpr2024_learning_23946,
  title = {{Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31056},
}

@inproceedings{aaai2025_open_10201,
  title = {{Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34247},
}

@inproceedings{icml2025_a_46477,
  title = {{A General Representation-Based Approach to Multi-Source Domain Adaptation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46089},
}

@inproceedings{cvpr2026_oslash_98867,
  title = {{\$oslash\$ Source Models Leak What They Shouldn't \$nrightarrow\$: Unlearning Zero-Shot Transfer in Domain Adaptation Through Adversarial Optimization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Devalapally_oslash_Source_Models_Leak_What_They_Shouldnt_nrightarrow_Unlearning_Zero-Shot_CVPR_2026_paper.html},
}

@inproceedings{aaai2026_robust_106309,
  title = {{Robust Domain Adaptive Hashing via Structural Noise Modeling and Correction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38616},
}

@inproceedings{iclr2025_selective_81756,
  title = {{Selective Unlearning via Representation Erasure Using Domain Adversarial Training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30023},
}

@inproceedings{neurips2026_dare_91340,
  title = {{DARE: Dual-Level Adversarial Learning with Domain-Aware Regularization for Whole Slide Image Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/154306},
}