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Unsupervised Source (data) Free domain adaptation (USFDA) aims to transfer knowledge from a well-trained source model to a related but unlabeled target domain.
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M. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks,” in International conference on machine learning
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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
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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
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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
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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
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S. Sankaranarayanan, Y. Balaji, C. D. Castillo, and R. Chellappa, “Generate to adapt: Aligning domains using generative adversarial networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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K. Saito, K. Watanabe, Y. Ushiku, and T. Harada, “Maximum classifier discrepancy for unsupervised domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
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H. Guo, R. Pasunuru, and M. Bansal, “Multi-source domain adaptation for text classification via distancenet-bandits,” in Proceedings of the AAAI Conference on Artificial Intelligence
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Y. Luo, Z. Huang, Z. Wang, Z. Zhang, and M. Baktashmotlagh, “Adversarial bipartite graph learning for video domain adaptation,” in Proceedings of the 28th ACM International Conference on Multimedia
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F. Nielsen and K. Sun, “Guaranteed deterministic bounds on the total variation distance between univariate mixtures,” in 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)
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M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in Proceedings of the European conference on computer vision (ECCV)
2018
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Y. Yao, Y. Zhang, X. Li, and Y. Ye, “Heterogeneous domain adaptation via soft transfer network,” in Proceedings of the 27th ACM international conference on multimedia
2019
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W. M. Kouw and M. Loog, “A review of domain adaptation without target labels,” IEEE transactions on pattern analysis and machine intelligence
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S. Li, C. H. Liu, B. Xie, L. Su, Z. Ding, and G. Huang, “Joint adversarial domain adaptation,” in Proceedings of the 27th ACM International Conference on Multimedia
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R. Müller, S. Kornblith, and G. Hinton, “When does label smoothing help?,” Advances in neural information processing systems
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X. Peng, Z. Huang, X. Sun, and K. Saenko, “Domain agnostic learning with disentangled representations,” in International Conference on Machine Learning
2019
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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
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2020
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2020
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Z. Pan, W. Yu, B. Wang, H. Xie, V. S. Sheng, J. Lei, and S. Kwong, “Loss functions of generative adversarial networks (gans): opportunities and challenges,” IEEE Transactions on Emerging Topics in Computational Intelligence
2020
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M. Wang, W. Wang, B. Li, X. Zhang, L. Lan, H. Tan, T. Liang, W. Yu, and Z. Luo, “Interbn: Channel fusion for adversarial unsupervised domain adaptation,” in Proceedings of the 29th ACM International Conference on Multimedia
2021
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Y. Cheng, F. Wei, J. Bao, D. Chen, F. Wen, and W. Zhang, “Dual path learning for domain adaptation of semantic segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2021
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W. Deng, Y. Cui, Z. Liu, G. Kuang, D. Hu, M. Pietikäinen, and L. Liu, “Informative class-conditioned feature alignment for unsupervised domain adaptation,” in Proceedings of the 29th ACM International Conference on Multimedia
2021
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M. Ye, J. Zhang, J. Ouyang, and D. Yuan, “Source data-free unsupervised domain adaptation for semantic segmentation,” in Proceedings of the 29th ACM International Conference on Multimedia
2021
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2021
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Y. Chen, Y. Pan, Y. Wang, T. Yao, X. Tian, and T. Mei, “Transferrable contrastive learning for visual domain adaptation,” in Proceedings of the 29th ACM International Conference on Multimedia
2021
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2021
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2021
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