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Source-free unsupervised domain adaptation (SFUDA) aims to learn a target domain model using unlabeled target data and the knowledge of a well-trained source domain model.
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J. Liang, R. He, Z. Sun, and T. Tan, “Exploring uncertainty in pseudo-label guided unsupervised domain adaptation,” Pattern Recognition , vol. 96, p. 106996, 2019
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2019
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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
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2021
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X. Li, J. Li, L. Zhu, G. Wang, and Z. Huang, “Imbalanced source-free domain adaptation,” in Proceedings of the 29th ACM International Conference on Multimedia , 2021, pp. 3330–3339
2021
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2021
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2021
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2021
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2021
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2021
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F. Wang, Z. Han, Y. Gong, and Y. Yin, “Exploring domain-invariant parameters for source free domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 7151–7160
2022
Closest in time.
Z. Han, H. Sun, and Y. Yin, “Learning transferable parameters for unsupervised domain adaptation,” IEEE Transactions on Image Processing , 2022
2022
Closest in time.
J. Moon, D. Das, and C. G. Lee, “A multi-stage framework with mean subspace computation and recursive feedback for online unsupervised domain adaptation,” IEEE Transactions on Image Processing , 2022
2022
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2022
Closest in time.
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Closest in time.