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Over the past decade, domain adaptation has become a widely studied branch of transfer learning that aims to improve performance on target domains by leveraging knowledge from the source domain.
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B. Chidlovskii, S. Clinchant, and G. Csurka, “Domain adaptation in the absence of source domain data,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2016, pp. 451–460
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2018
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2018
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K. Saito, D. Kim, S. Sclaroff, T. Darrell, and K. Saenko, “Semi-supervised domain adaptation via minimax entropy,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 8050–8058
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C.-Y. Lee, T. Batra, M. H. Baig, and D. Ulbricht, “Sliced wasserstein discrepancy for unsupervised domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 285–10 295
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2019
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2021
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2021
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H. Yan, Y. Guo, and C. Yang, “Augmented self-labeling for source-free unsupervised domain adaptation,” in NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications , 2021
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J. Li, E. Chen, Z. Ding, L. Zhu, K. Lu, and H. T. Shen, “Maximum density divergence for domain adaptation,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 11, pp. 3918–3930, 2020
2020
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G. Wilson and D. J. Cook, “A survey of unsupervised deep domain adaptation,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 11, no. 5, pp. 1–46, 2020
2020
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J. Liang, D. Hu, and J. Feng, “Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,” in International Conference on Machine Learning . PMLR, 2020, pp. 6028–6039
2020
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J. N. Kundu, N. Venkat, R. V. Babu et al. , “Universal source-free domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4544–4553
2020
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2020
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2020
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2021
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2023
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