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Source-Free Domain Adaptation (SFDA) aims to solve the domain adaptation problem by transferring the knowledge learned from a pre-trained source model to an unseen target domain.
Classification and analysis of multivariate observations
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Domain separation networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
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Learning discrete representations via information maximizing self-augmented training
Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 2017
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Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2017
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Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Domain adaptive faster r-cnn for object detection in the wild
Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool · 2018
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Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification
Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, and Jianbin Jiao · 2018
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Image to image translation for domain adaptation
Zak Murez, Soheil Kolouri, David Kriegman, Ravi Ramamoorthi, and Kyungnam Kim · 2018
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A dirt-t approach to unsupervised domain adaptation
Rui Shu, Hung H Bui, Hirokazu Narui, and Stefano Ermon · 2018
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Yang Zou, Zhiding Yu, BVK Kumar, and Jinsong Wang · 2018
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Cluster alignment with a teacher for unsupervised domain adaptation
Zhijie Deng, Yucen Luo, and Jun Zhu · 2019
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
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Unsupervised domain adaptation in the absence of source data
Roshni Sahoo, Divya Shanmugam, and John Guttag · 2020
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Adapting object detectors with conditional domain normalization
Peng Su, Kun Wang, Xingyu Zeng, Shixiang Tang, Dapeng Chen, Di Qiu, and Xiaogang Wang · 2020
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Unsupervised domain adaptation via structurally regularized deep clustering
Hui Tang, Ke Chen, and Kui Jia · 2020
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Unsupervised domain adaptation in semantic segmentation: a review
Marco Toldo, Andrea Maracani, Umberto Michieli, and Pietro Zanuttigh · 2020
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Sliced wasserstein discrepancy for unsupervised domain adaptation
Chen-Yu Lee, Tanmay Batra, Mohammad Haris Baig, and Daniel Ulbricht · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Interpolation consistency training for semi-supervised learning
Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz · 2019
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Beyond intra-modality: A survey of heterogeneous person re-identification
Zheng Wang, Zhixiang Wang, Yinqiang Zheng, Yang Wu, Wenjun Zeng, and Shin’ichi Satoh · 2019
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Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation
Ruijia Xu, Guanbin Li, Jihan Yang, and Liang Lin · 2019
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Domain-symmetric networks for adversarial domain adaptation
Yabin Zhang, Hui Tang, Kui Jia, and Mingkui Tan · 2019
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Pseudo-labeling and confirmation bias in deep semi-supervised learning
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2020
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Dual mixup regularized learning for adversarial domain adaptation
Yuan Wu, Diana Inkpen, and Ahmed El-Roby · 2020
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Reliable weighted optimal transport for unsupervised domain adaptation
Renjun Xu, Pelen Liu, Liyan Wang, Chao Chen, and Jindong Wang · 2020
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Unsupervised domain adaptation without source data by casting a bait
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2020
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Fast batch nuclear-norm maximization and minimization for robust domain adaptation
Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, and Qi Tian · 2021
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Domain adaptation without source data
Youngeun Kim, Donghyeon Cho, Kyeongtak Han, Priyadarshini Panda, and Sungeun Hong · 2021
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Lamda: Label matching deep domain adaptation
Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, and Dinh Phung · 2021
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Source-free domain adaptation for semantic segmentation
Yuang Liu, Wei Zhang, and Jun Wang · 2021
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Source-free domain adaptation via avatar prototype generation and adaptation
Z. Qiu, Y. Zhang, H. Lin, S. Niu, and M. Tan · 2021
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Nearest neighborhood-based deep clustering for source data-absent unsupervised domain adaptation
Song Tang, Yan Yang, Zhiyuan Ma, Norman Hendrich, Fanyu Zeng, Shuzhi Sam Ge, Changshui Zhang, and Jianwei Zhang · 2021
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Vdm-da: Virtual domain modeling for source data-free domain adaptation
Jiayi Tian, Jing Zhang, Wen Li, and Dong Xu · 2021
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Adaptive adversarial network for source-free domain adaptation
Haifeng Xia, Handong Zhao, and Zhengming Ding · 2021
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