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In theory, the success of unsupervised domain adaptation (UDA) largely relies on domain gap estimation.
M. Xu, J. Zhang, B. Ni, T. Li, C. Wang, Q. Tian, W. Zhang, Adversarial domain adaptation with domain mixup (2019) · 1912
Earlier work this paper cites.
G. Yang, H. Xia, M. Ding, Z. Ding, Bi-directional generation for unsupervised domain adaptation (2020) · 2002
Earlier work this paper cites.
L. Van der Maaten, G. Hinton, Visualizing data using t-sne., Journal of machine learning research 9 (11) (2008)
2008
Earlier work this paper cites.
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, J. W. Vaughan, A theory of learning from different domains, Machine learning 79 (1) (2010) 151–175
2010
Earlier work this paper cites.
A. Krause, P. Perona, R. Gomes, Discriminative clustering by regularized information maximization, Advances in neural information processing systems 23 (2010)
2010
Earlier work this paper cites.
I. Sutskever, J. Martens, G. Dahl, G. Hinton, On the importance of initialization and momentum in deep learning, in: International conference on machine learning, PMLR, 2013, pp. 1139–1147
2013
Earlier work this paper cites.
Y. Ganin, V. Lempitsky, Unsupervised domain adaptation by backpropagation, in: International conference on machine learning, PMLR, 2015, pp. 1180–1189
2015
Earlier work this paper cites.
S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift , in: F. Bach, D. Blei (Eds.), Proceedings of the 32nd International Conference on Machine Learning, Vol. 37 of Proceedings of Machine Learning Research, PMLR, Lille, France, 2015, pp. 448–456. URL https://proceedings.mlr.press/v37/ioffe15.html
2015
Earlier work this paper cites.
M. Long, Y. Cao, J. Wang, M. Jordan, Learning transferable features with deep adaptation networks, in: International conference on machine learning, PMLR, 2015, pp. 97–105
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778
2016
Earlier work this paper cites.
2017
Cited alongside, same era.
H. Venkateswara, J. Eusebio, S. Chakraborty, S. Panchanathan, Deep hashing network for unsupervised domain adaptation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 5018–5027
2017
Cited alongside, same era.
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, D. Krishnan, Unsupervised pixel-level domain adaptation with generative adversarial networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3722–3731
2017
Cited alongside, same era.
E. Tzeng, J. Hoffman, K. Saenko, T. Darrell, Adversarial discriminative domain adaptation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7167–7176
2017
Cited alongside, same era.
2020
Later among the works it cites.
Y. Zhang, B. Deng, H. Tang, L. Zhang, K. Jia, Unsupervised multi-class domain adaptation: Theory, algorithms, and practice, IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)
2020
Later among the works it cites.
Z. Lu, Y. Yang, X. Zhu, C. Liu, Y.-Z. Song, 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
Later among the works it cites.
R. Li, Q. Jiao, W. Cao, H.-S. Wong, 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.
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K. Saito, K. Watanabe, Y. Ushiku, T. Harada, Maximum classifier discrepancy for unsupervised domain adaptation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 3723–3732
2018
Cited alongside, same era.
K. Saito, Y. Ushiku, T. Harada, K. Saenko, Adversarial dropout regularization (2018) · 2018
Cited alongside, same era.
J. Li, E. Chen, Z. Ding, L. Zhu, K. Lu, Z. Huang, Cycle-consistent conditional adversarial transfer networks, in: Proceedings of the 27th ACM International Conference on Multimedia, 2019, pp. 747–755
2019
Cited alongside, same era.
C.-Y. Lee, T. Batra, M. H. Baig, D. Ulbricht, Sliced wasserstein discrepancy for unsupervised domain adaptation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 10285–10295
2019
Cited alongside, same era.
R. Xu, G. Li, J. Yang, 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.
S. Li, C. H. Liu, B. Xie, L. Su, Z. Ding, G. Huang, Joint adversarial domain adaptation, in: Proceedings of the 27th ACM International Conference on Multimedia, 2019, pp. 729–737
2019
Cited alongside, same era.
J. Liang, D. Hu, 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
Cited alongside, same era.
2020
Later among the works it cites.
S. Ye, K. Wu, M. Zhou, Y. Yang, S. H. Tan, K. Xu, J. Song, C. Bao, K. Ma, Light-weight calibrator: a separable component for unsupervised domain adaptation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 13736–13745
2020
Later among the works it cites.
H. Tang, K. Chen, K. Jia, Unsupervised domain adaptation via structurally regularized deep clustering, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 8725–8735
2020
Later among the works it cites.
S. Yang, Y. Wang, J. van de Weijer, L. Herranz, S. Jui, Generalized source-free domain adaptation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 8978–8987
2021
Later among the works it cites.
W. Lee, H. Kim, J. Lee, Compact class-conditional domain invariant learning for multi-class domain adaptation, Pattern Recognition 112 (2021) 107763
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Wang, J. Chen, J. Lin, L. Sigal, C. W. de Silva, Discriminative feature alignment: Improving transferability of unsupervised domain adaptation by gaussian-guided latent alignment, Pattern Recognition 116 (2021) 107943
2021
Later among the works it cites.