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Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain.
S. Ben-David, J. Blitzer, K. Crammer, F. Pereira et al. , “Analysis of representations for domain adaptation,” Proc. NeurIPS , 2007
2007
Earlier work this paper cites.
J. Yang, R. Yan, and A. G. Hauptmann, “Cross-domain video concept detection using adaptive svms,” in Proc. ACM-MM , 2007
2007
Earlier work this paper cites.
K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in Proc. ECCV , 2010
2010
Earlier work this paper cites.
T. Tommasi, F. Orabona, and B. Caputo, “Safety in numbers: Learning categories from few examples with multi model knowledge transfer,” in Proc. CVPR , 2010
2010
Earlier work this paper cites.
K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in Proc. ECCV , 2010
2010
Earlier work this paper cites.
I. Kuzborskij and F. Orabona, “Stability and hypothesis transfer learning,” in Proc. ICML , 2013
2013
Earlier work this paper cites.
M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu, “Transfer feature learning with joint distribution adaptation,” in Proc. ICCV , 2013
2013
Earlier work this paper cites.
Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in Proc. ICML , 2015
2015
Earlier work this paper cites.
M. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks,” in Proc. ICML , 2015
2015
Earlier work this paper cites.
B. Sun and K. Saenko, “Deep coral: Correlation alignment for deep domain adaptation,” in Proc. ECCV Workshops , 2016
2016
Earlier work this paper cites.
B. Chidlovskii, S. Clinchant, and G. Csurka, “Domain adaptation in the absence of source domain data,” in Proc. KDD , 2016
2016
Earlier work this paper cites.
J. Xie, R. Girshick, and A. Farhadi, “Unsupervised deep embedding for clustering analysis,” in Proc. ICML , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. CVPR , 2016
2016
Earlier work this paper cites.
W. Zellinger, T. Grubinger, E. Lughofer, T. Natschläger, and S. Saminger-Platz, “Central moment discrepancy (cmd) for domain-invariant representation learning,” in Proc. ICLR , 2017
2017
Earlier work this paper cites.
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in Proc. CVPR , 2017
2017
Earlier work this paper cites.
P. Panareda Busto and J. Gall, “Open set domain adaptation,” in Proc. ICCV , 2017
2017
Earlier work this paper cites.
S. Laine and T. Aila, “Temporal ensembling for semi-supervised learning,” in Proc. ICLR , 2017
2017
Earlier work this paper cites.
H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan, “Deep hashing network for unsupervised domain adaptation,” in Proc. CVPR , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proc. CVPR , 2017
2017
Earlier work this paper cites.
S. Li, S. Song, G. Huang, Z. Ding, and C. Wu, “Domain invariant and class discriminative feature learning for visual domain adaptation,” IEEE Transactions on Image Processing , vol. 27, no. 9, pp. 4260–4273, 2018
2018
Earlier work this paper cites.
M. Long, Z. Cao, J. Wang, and M. I. Jordan, “Conditional adversarial domain adaptation,” in Proc. NeurIPS , 2018
2018
Earlier work this paper cites.
Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker, “Learning to adapt structured output space for semantic segmentation,” in Proc. CVPR , 2018
2018
Earlier work this paper cites.
Y. Zou, Z. Yu, B. Kumar, and J. Wang, “Unsupervised domain adaptation for semantic segmentation via class-balanced self-training,” in Proc. ECCV , 2018
2018
Earlier work this paper cites.
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,” Proc. ICLR , 2018
2018
Earlier work this paper cites.
Z. Cao, M. Long, J. Wang, and M. I. Jordan, “Partial transfer learning with selective adversarial networks,” in Proc. CVPR , 2018
2018
Earlier work this paper cites.
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. Efros, and T. Darrell, “Cycada: Cycle-consistent adversarial domain adaptation,” in Proc. ICML , 2018
2018
Cited alongside, same era.
K. Saito, K. Watanabe, Y. Ushiku, and T. Harada, “Maximum classifier discrepancy for unsupervised domain adaptation,” in Proc. CVPR , 2018
2018
Cited alongside, same era.
K. Saito, Y. Ushiku, T. Harada, and K. Saenko, “Adversarial dropout regularization,” in Proc. ICLR , 2018
2018
Cited alongside, same era.
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, “Mixmatch: A holistic approach to semi-supervised learning,” in Proc. NeurIPS , 2019
2019
Cited alongside, same era.
K. You, M. Long, Z. Cao, J. Wang, and M. I. Jordan, “Universal domain adaptation,” in Proc. CVPR , 2019
2019
Cited alongside, same era.
Y. Wu, D. Inkpen, and A. El-Roby, “Dual mixup regularized learning for adversarial domain adaptation,” in Proc. ECCV , 2020
2020
Later among the works it cites.
J. E. Van Engelen and H. H. Hoos, “A survey on semi-supervised learning,” Machine Learning , vol. 109, p. 373–440, 2020
2020
Later among the works it cites.
X. Wang, D. Kihara, J. Luo, and G.-J. Qi, “Enaet: A self-trained framework for semi-supervised and supervised learning with ensemble transformations,” IEEE Transactions on Image Processing , vol. 30, pp. 1639–1647, 2020
2020
Later among the works it cites.
K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. D. Cubuk, A. Kurakin, and C.-L. Li, “Fixmatch: Simplifying semi-supervised learning with consistency and confidence,” in Proc. NeurIPS , 2020
2020
Later among the works it cites.
G. Yang, H. Xia, M. Ding, and Z. Ding, “Bi-directional generation for unsupervised domain adaptation,” in Proc. AAAI , 2020
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J. Liang, R. He, Z. Sun, and T. Tan, “Distant supervised centroid shift: A simple and efficient approach to visual domain adaptation,” in Proc. CVPR , 2019
2019
Cited alongside, same era.
J. Li, S. Wu, C. Liu, Z. Yu, and H.-S. Wong, “Semi-supervised deep coupled ensemble learning with classification landmark exploration,” IEEE Transactions on Image Processing , vol. 29, pp. 538–550, 2019
2019
Cited alongside, same era.
R. Müller, S. Kornblith, and G. Hinton, “When does label smoothing help?” Proc. NeurIPS , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
R. Xu, G. Li, J. Yang, and L. Lin, “Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation,” in Proc. ICCV , 2019
2019
Cited alongside, same era.
Y. Zhang, H. Tang, K. Jia, and M. Tan, “Domain-symmetric networks for adversarial domain adaptation,” in Proc. CVPR , 2019
2019
Cited alongside, same era.
Y. Zhang, T. Liu, M. Long, and M. Jordan, “Bridging theory and algorithm for domain adaptation,” in Proc. ICML , 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
H. Tang, K. Chen, and K. Jia, “Unsupervised domain adaptation via structurally regularized deep clustering,” in Proc. CVPR , 2020
2020
Later among the works it cites.
X. Gu, J. Sun, and Z. Xu, “Spherical space domain adaptation with robust pseudo-label loss,” in Proc. CVPR , 2020
2020
Later among the works it cites.
R. Xu, P. Liu, L. Wang, C. Chen, and J. Wang, “Reliable weighted optimal transport for unsupervised domain adaptation,” in Proc. CVPR , 2020
2020
Later among the works it cites.
Z. Lu, Y. Yang, X. Zhu, C. Liu, Y.-Z. Song, and T. Xiang, “Stochastic classifiers for unsupervised domain adaptation,” in Proc. CVPR , 2020
2020
Later among the works it cites.
P. Dai, P. Chen, Q. Wu, X. Hong, Q. Ye, Q. Tian, C.-W. Lin, and R. Ji, “Disentangling task-oriented representations for unsupervised domain adaptation,” IEEE Transactions on Image Processing , vol. 31, pp. 1012–1026, 2021
2021
Later among the works it cites.
J. Tian, J. Zhang, W. Li, and D. Xu, “Vdm-da: Virtual domain modeling for source data-free domain adaptation,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
2021
Later among the works it cites.
Z. Qiu, Y. Zhang, H. Lin, S. Niu, Y. Liu, Q. Du, and M. Tan, “Source-free domain adaptation via avatar prototype generation and adaptation,” in Proc. IJCAI , 2021
2021
Later among the works it cites.
J. Liang, D. Hu, Y. Wang, R. He, and J. Feng, “Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Later among the works it cites.
H. Xia, H. Zhao, and Z. Ding, “Adaptive adversarial network for source-free domain adaptation,” in Proc. CVPR , 2021
2021
Later among the works it cites.
J. Huang, D. Guan, A. Xiao, and S. Lu, “Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data,” Proc. NeurIPS , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Liang, D. Hu, and J. Feng, “Domain adaptation with auxiliary target domain-oriented classifier,” in Proc. CVPR , 2021
2021
Later among the works it cites.
H. Liu, J. Wang, and M. Long, “Cycle self-training for domain adaptation,” in Proc. NeurIPS , 2021
2021
Later among the works it cites.
S. Yang, J. van de Weijer, L. Herranz, S. Jui et al. , “Exploiting the intrinsic neighborhood structure for source-free domain adaptation,” Proc. NeurIPS , 2021
2021
Later among the works it cites.
H. Yan, Y. Guo, and C. Yang, “Source-free unsupervised domain adaptation with surrogate data generation,” in Proc. BMVC , 2021
2021
Later among the works it cites.
Y. Qin, H. Wu, X. Zhang, and G. Feng, “Semi-supervised structured subspace learning for multi-view clustering,” IEEE Transactions on Image Processing , vol. 31, pp. 1–14, 2021
2021
Later among the works it cites.
K. Tanwisuth, X. Fan, H. Zheng, S. Zhang, H. Zhang, B. Chen, and M. Zhou, “A prototype-oriented framework for unsupervised domain adaptation,” Proc. NeurIPS , 2021
2021
Later among the works it cites.
D. Berthelot, R. Roelofs, K. Sohn, N. Carlini, and A. Kurakin, “Adamatch: A unified approach to semi-supervised learning and domain adaptation,” in Proc. ICLR , 2022
2022
Closest in time.
B. Xu, Z. Zeng, C. Lian, and Z. Ding, “Few-shot domain adaptation via mixup optimal transport,” IEEE Transactions on Image Processing , vol. 31, pp. 2518–2528, 2022
2022
Closest in time.