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Most existing studies on unsupervised domain adaptation (UDA) assume that each domain's training samples come with domain labels (e.g., painting, photo).
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2017
Cited alongside, same era.
B. Bhushan Damodaran, B. Kellenberger, R. Flamary, D. Tuia, and N. Courty, “Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation,” in ECCV , 2018
2018
Cited alongside, same era.
R. Xu, Z. Chen, W. Zuo, J. Yan, and L. Lin, “Deep cocktail network: Multi-source unsupervised domain adaptation with category shift,” in CVPR , 2018
2018
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H. Zhao, S. Zhang, G. Wu, J. M. Moura, J. P. Costeira, and G. J. Gordon, “Adversarial multiple source domain adaptation,” in NeurIPS , 2018
2018
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K. Saito, K. Watanabe, Y. Ushiku, and T. Harada, “Maximum classifier discrepancy for unsupervised domain adaptation,” in CVPR , 2018
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2020
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2020
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2020
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S. Zhao, G. Wang, S. Zhang, Y. Gu, Y. Li, Z. Song, P. Xu, R. Hu, H. Chai, and K. Keutzer, “Multi-source distilling domain adaptation,” in AAAI , 2020
2020
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Y. Li, L. Yuan, Y. Chen, P. Wang, and N. Vasconcelos, “Dynamic transfer for multi-source domain adaptation,” in CVPR , 2021
2021
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Y. Zuo, H. Yao, and C. Xu, “Attention-based multi-source domain adaptation,” IEEE Transactions on Image Processing , vol. 30, pp. 3793–3803, 2021
2021
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2021
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K. Zhou, Y. Yang, Y. Qiao, and T. Xiang, “Domain adaptive ensemble learning,” IEEE Transactions on Image Processing , vol. 30, pp. 8008–8018, 2021
2021
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A. Sharma, T. Kalluri, and M. Chandraker, “Instance level affinity-based transfer for unsupervised domain adaptation,” in CVPR , 2021
2021
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Y.-W. Luo and C.-X. Ren, “Conditional bures metric for domain adaptation,” in CVPR , 2021
2021
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C.-X. Ren, Y.-H. Liu, X.-W. Zhang, and K.-K. Huang, “Multi-source unsupervised domain adaptation via pseudo target domain,” IEEE Transactions on Image Processing , vol. 31, pp. 2122–2135, 2022
2022
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