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Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain.
A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf, and A. Smola, “A kernel method for the two-sample-problem,” in Advances in Neural Information Processing Systems , 2006, pp. 513–520
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S. Ben-David, J. Blitzer, K. Crammer, F. Pereira et al. , “Analysis of representations for domain adaptation,” in Advances in Neural Information Processing Systems , 2007, pp. 137–144
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2010
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2015
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2015
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2016
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B. Sun and K. Saenko, “Deep coral: Correlation alignment for deep domain adaptation,” in Proceedings of the European Conference on Computer Vision , 2016, pp. 443–450
2016
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K. Sohn, “Improved deep metric learning with multi-class n-pair loss objective,” in Advances in Neural Information Processing Systems , 2016, pp. 1857–1865
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
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K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan, “Unsupervised pixel-level domain adaptation with generative adversarial networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2017, pp. 3722–3731
2017
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M. Long, H. Zhu, J. Wang, and M. I. Jordan, “Deep transfer learning with joint adaptation networks,” in International Conference on Machine Learning , 2017, pp. 2208–2217
2017
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E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2017, pp. 7167–7176
2017
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2017
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W. Zellinger, T. Grubinger, E. Lughofer, T. Natschläger, and S. Saminger-Platz, “Central moment discrepancy (cmd) for domain-invariant representation learning,” in International Conference on Learning Representations , 2017
2017
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M. Long, Z. Cao, J. Wang, and M. I. Jordan, “Conditional adversarial domain adaptation,” in Advances in Neural Information Processing Systems , 2018, pp. 1647–1657
2018
Cited alongside, same era.
K. Saito, Y. Ushiku, T. Harada, and K. Saenko, “Adversarial dropout regularization,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
K. Saito, K. Watanabe, Y. Ushiku, and T. Harada, “Maximum classifier discrepancy for unsupervised domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 3723–3732
2018
Cited alongside, same era.
2018
Cited alongside, same era.
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 , 2020, pp. 6028–6039
2020
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S. Cui, S. Wang, J. Zhuo, C. Su, Q. Huang, and Q. Tian, “Gradually vanishing bridge for adversarial domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 12 455–12 464
2020
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L. Hu, M. Kan, S. Shan, and X. Chen, “Unsupervised domain adaptation with hierarchical gradient synchronization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4043–4052
2020
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S. Cui, S. Wang, J. Zhuo, L. Li, Q. Huang, and Q. Tian, “Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3941–3950
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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
2019
Cited alongside, same era.
Y. Zhang, T. Liu, M. Long, and M. Jordan, “Bridging theory and algorithm for domain adaptation,” in International Conference on Machine Learning , 2019, pp. 7404–7413
2019
Cited alongside, same era.
H. Yan, Z. Li, Q. Wang, P. Li, Y. Xu, and W. Zuo, “Weighted and class-specific maximum mean discrepancy for unsupervised domain adaptation,” IEEE Transactions on Multimedia , vol. 22, no. 9, pp. 2420–2433, 2019
2019
Cited alongside, same era.
G. Kang, L. Jiang, Y. Yang, and A. G. Hauptmann, “Contrastive adaptation network for unsupervised domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 4893–4902
2019
Cited alongside, same era.
Y. Zheng, X. Wang, G. Zhang, B. Xiao, F. Xiao, and J. Zhang, “Multi-kernel coupled projections for domain adaptive dictionary learning,” IEEE Transactions on Multimedia , vol. 21, no. 9, pp. 2292–2304, 2019
2019
Cited alongside, same era.
X. Ma, T. Zhang, and C. Xu, “Deep multi-modality adversarial networks for unsupervised domain adaptation,” IEEE Transactions on Multimedia , vol. 21, no. 9, pp. 2419–2431, 2019
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 Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 1426–1435
2019
Cited alongside, same era.
W.-G. Chang, T. You, S. Seo, S. Kwak, and B. Han, “Domain-specific batch normalization for unsupervised domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 7354–7362
2019
Cited alongside, same era.
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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9111–9120
2020
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P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learning,” in Advances in Neural Information Processing Systems , 2020, pp. 18 661–18 673
2020
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Y. Ge, F. Zhu, D. Chen, R. Zhao, and h. Li, “Self-paced contrastive learning with hybrid memory for domain adaptive object re-id,” in Advances in Neural Information Processing Systems , 2020, pp. 11 309–11 321
2020
Later among the works it cites.
S. Dai, Y. Cheng, Y. Zhang, Z. Gan, J. Liu, and L. Carin, “Contrastively smoothed class alignment for unsupervised domain adaptation,” in Proceedings of the Asian Conference on Computer Vision , 2020, pp. 268–283
2020
Later among the works it cites.
2020
Later among the works it cites.
R. Li, Q. Jiao, W. Cao, H.-S. Wong, and S. Wu, “Model adaptation: Unsupervised domain adaptation without source data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9641–9650
2020
Later among the works it cites.
D. Li, Y. Lu, W. Wang, Z. Lai, J. Zhou, and X. Li, “Discriminative invariant alignment for unsupervised domain adaptation,” IEEE Transactions on Multimedia , 2021
2021
Closest in time.
X. Jin, C. Lan, W. Zeng, and Z. Chen, “Style normalization and restitution for domain generalization and adaptation,” IEEE Transactions on Multimedia , 2021
2021
Closest in time.
M. Thota and G. Leontidis, “Contrastive domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2021, pp. 2209–2218
2021
Closest in time.
M. Toldo, U. Michieli, and P. Zanuttigh, “Unsupervised domain adaptation in semantic segmentation via orthogonal and clustered embeddings,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 1358–1368
2021
Closest in time.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of Machine Learning Research , vol. 9, no. 11, pp. 2579–2605, 2008
2021
Closest in time.