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Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain.
C. E. Shannon, “A mathematical theory of communication,” Bell system technical journal , vol. 27, no. 3, pp. 379–423, 1948
1948
Earlier work this paper cites.
G. A. Miller, “Wordnet: a lexical database for english,” Communications of the ACM , vol. 38, no. 11, pp. 39–41, 1995
1995
Earlier work this paper cites.
T. Joachims et al. , “Transductive inference for text classification using support vector machines,” in Icml , vol. 99, 1999, pp. 200–209
1999
Earlier work this paper cites.
T. Papadopoulo and M. I. Lourakis, “Estimating the jacobian of the singular value decomposition: Theory and applications,” in European Conference on Computer Vision . Springer, 2000, pp. 554–570
2000
Earlier work this paper cites.
M. Fazel, “Matrix rank minimization with applications,” 2002
2002
Earlier work this paper cites.
Y. Grandvalet and Y. Bengio, “Semi-supervised learning by entropy minimization,” in Advances in neural information processing systems , 2005, pp. 529–536
2005
Earlier work this paper cites.
N. Srebro, J. Rennie, and T. S. Jaakkola, “Maximum-margin matrix factorization,” in Advances in neural information processing systems , 2005, pp. 1329–1336
2005
Earlier work this paper cites.
H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on knowledge and data engineering , vol. 21, no. 9, pp. 1263–1284, 2009
2009
Earlier work this paper cites.
E. J. Candès and B. Recht, “Exact matrix completion via convex optimization,” Foundations of Computational mathematics , vol. 9, no. 6, p. 717, 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” 2009
2009
Earlier work this paper cites.
K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in European conference on computer vision . Springer, 2010, pp. 213–226
2010
Earlier work this paper cites.
J.-F. Cai, E. J. Candès, and Z. Shen, “A singular value thresholding algorithm for matrix completion,” SIAM Journal on optimization , vol. 20, no. 4, pp. 1956–1982, 2010
2010
Earlier work this paper cites.
B. Recht, M. Fazel, and P. A. Parrilo, “Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization,” SIAM review , vol. 52, no. 3, pp. 471–501, 2010
2010
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” Journal of Machine Learning Research , vol. 13, no. Mar, pp. 723–773, 2012
2012
Earlier work this paper cites.
A. Kulesza, B. Taskar et al. , “Determinantal point processes for machine learning,” Foundations and Trends® in Machine Learning , vol. 5, no. 2–3, pp. 123–286, 2012
2012
Earlier work this paper cites.
W. Dong, G. Shi, and X. Li, “Nonlocal image restoration with bilateral variance estimation: a low-rank approach,” IEEE transactions on image processing , vol. 22, no. 2, pp. 700–711, 2012
2012
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
S. Gu, L. Zhang, W. Zuo, and X. Feng, “Weighted nuclear norm minimization with application to image denoising,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 2862–2869
2014
Earlier work this paper cites.
J. Pennington, R. Socher, and C. Manning, “Glove: Global vectors for word representation,” in Conference on Empirical Methods in Natural Language Processing , 2014, pp. 1532–1543
2014
Earlier work this paper cites.
M. Long, Y. Cao, J. Wang, and M. I. Jordan, “Learning transferable features with deep adaptation networks,” in Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015 , 2015, pp. 97–105
2015
Earlier work this paper cites.
B. Sun and K. Saenko, “Deep coral: Correlation alignment for deep domain adaptation,” in European Conference on Computer Vision . Springer, 2016, pp. 443–450
2016
Earlier work this paper cites.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 2096–2030, 2016
2016
Earlier work this paper cites.
M. Long, H. Zhu, J. Wang, and M. I. Jordan, “Unsupervised domain adaptation with residual transfer networks,” in NIPS , 2016, pp. 136–144. [Online]. Available: http://papers.nips.cc/paper/6110-unsupervised-domain-adaptation-with-residual-transfer-networks
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and 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
Cited alongside, same era.
2016
Cited alongside, same era.
H. Yan, Y. Ding, P. Li, Q. Wang, Y. Xu, and W. Zuo, “Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 2272–2281
2017
Cited alongside, same era.
S. Sankaranarayanan, Y. Balaji, C. D. Castillo, and R. Chellappa, “Generate to adapt: Aligning domains using generative adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8503–8512
2018
Later among the works it cites.
X. Wang, Y. Ye, and A. Gupta, “Zero-shot recognition via semantic embeddings and knowledge graphs,” in CVPR , 2018, pp. 6857–6866
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
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2017
Cited alongside, same era.
M. Long, H. Zhu, J. Wang, and M. I. Jordan, “Deep transfer learning with joint adaptation networks,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 2208–2217
2017
Cited alongside, same era.
J. Zhuo, S. Wang, W. Zhang, and Q. Huang, “Deep unsupervised convolutional domain adaptation,” in Proceedings of the 25th ACM international conference on Multimedia . ACM, 2017, pp. 261–269
2017
Cited alongside, same era.
E. Tzeng, J. Hoffman, K. Saenko, and 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.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in International Conference on Machine Learning , 2017, pp. 214–223
2017
Cited alongside, same era.
H. Venkateswara, J. Eusebio, S. Chakraborty, and 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.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Later among the works it cites.
T.-H. Vu, H. Jain, M. Bucher, M. Cord, and P. Pérez, “Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2517–2526
2019
Later among the works it cites.
Y. Zou, Z. Yu, X. Liu, B. Kumar, and J. Wang, “Confidence regularized self-training,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 5982–5991
2019
Later among the works it cites.
J. Zhuo, S. Wang, S. Cui, and Q. Huang, “Unsupervised open domain recognition by semantic discrepancy minimization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 750–759
2019
Later among the works it cites.
R. Xu, G. Li, J. Yang, and L. Lin, “Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation,” in The IEEE International Conference on Computer Vision (ICCV) , October 2019
2019
Later among the works it cites.
X. Peng, Q. Bai, X. Xia, Z. Huang, K. Saenko, and B. Wang, “Moment matching for multi-source domain adaptation,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 1406–1415
2019
Later among the works it cites.
K. Saito, D. Kim, S. Sclaroff, T. Darrell, and K. Saenko, “Semi-supervised domain adaptation via minimax entropy,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 8050–8058
2019
Later among the works it cites.
Y. Zou, Z. Yu, X. Liu, B. V. Kumar, and J. Wang, “Confidence regularized self-training,” in The IEEE International Conference on Computer Vision (ICCV) , October 2019
2019
Later among the works it cites.
X. Chen, S. Wang, M. Long, and J. Wang, “Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation,” in International Conference on Machine Learning , 2019, pp. 1081–1090
2019
Later among the works it cites.
Y. Zhang, H. Tang, K. Jia, and M. Tan, “Domain-symmetric networks for adversarial domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 5031–5040
2019
Later among the works it cites.
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
Later among the works it cites.
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 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
Later among the works it cites.
S. Cui, X. Jin, S. Wang, Y. He, and Q. Huang, “Heuristic domain adaptation,” in Advances in Neural Information Processing Systems , 2020
2020
Later among the works it cites.
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
Later among the works it cites.
Y. Jin, X. Wang, M. Long, and J. Wang, “Minimum class confusion for versatile domain adaptation,” in Proceedings of the European conference on computer vision (ECCV) , 2020, pp. 464–480
2020
Later among the works it cites.
S. Cui, S. Wang, J. Zhuo, C. Su, Q. Huang, and T. Qi, “Gradually vanishing bridge for adversarial domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020
2020
Later among the works it cites.
Z. Qi, S. Wang, C. Su, L. Su, Q. Huang, and Q. Tian, “Self-regulated learning for egocentric video activity anticipation,” IEEE Transactions on Pattern Analysis & Machine Intelligence , no. 01, pp. 1–1, 2021
2021
Closest in time.
B. Gong, Y. Shi, F. Sha, and K. Grauman, “Geodesic flow kernel for unsupervised domain adaptation,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2012, pp. 2066–2073
2073
Closest in time.
M. Chen, H. Xue, and D. Cai, “Domain adaptation for semantic segmentation with maximum squares loss,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 2090–2099
2099
Closest in time.