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Unsupervised domain adaptation (UDA) involves adapting a model trained on a label-rich source domain to an unlabeled target domain.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Validating clusters using the hopkins statistic
Amit Banerjee and Rajesh N Dave · 2004
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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A kernel method for the two-sample-problem
Arthur Gretton, Karsten M Borgwardt, Malte Rasch, Bernhard Schölkopf, and Alex J Smola · 2007
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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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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Adversarial machine learning
Ling Huang, Anthony D Joseph, Blaine Nelson, Benjamin IP Rubinstein, and J Doug Tygar · 2011
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Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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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-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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Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
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Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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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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Minimal-entropy correlation alignment for unsupervised deep domain adaptation
Pietro Morerio, Jacopo Cavazza, and Vittorio Murino · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 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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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Minimal-entropy correlation alignment for unsupervised deep domain adaptation
Pietro Morerio, Jacopo Cavazza, and Vittorio Murino · 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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Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
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Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation
Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang · 2019
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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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Contrastive adaptation network for unsupervised domain adaptation
Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann · 2019
Domain adaptation for medical image analysis: a survey
Hao Guan and Mingxia Liu · 2021
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Cross-domain adaptive clustering for semi-supervised domain adaptation
Jichang Li, Guanbin Li, Yemin Shi, and Yizhou Yu · 2021
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Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer
Jian Liang, Dapeng Hu, Yunbo Wang, Ran He, and Jiashi Feng · 2021
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Cycle self-training for domain adaptation
Hong Liu, Jianmin Wang, and Mingsheng Long · 2021
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Active universal domain adaptation
Xinhong Ma, Junyu Gao, and Changsheng Xu · 2021
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Tune it the right way: Unsupervised validation of domain adaptation via soft neighborhood density
Kuniaki Saito, Donghyun Kim, Piotr Teterwak, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2021
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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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Transferability and hardness of supervised classification tasks
Anh T Tran, Cuong V Nguyen, and Tal Hassner · 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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Towards accurate model selection in deep unsupervised domain adaptation
Kaichao You, Ximei Wang, Mingsheng Long, and Michael Jordan · 2019
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Bridging theory and algorithm for domain adaptation
Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael Jordan · 2019
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Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective
Muhammad Abdullah Jamal, Matthew Brown, Ming-Hsuan Yang, Liqiang Wang, and Boqing Gong · 2020
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Partial video domain adaptation with partial adversarial temporal attentive network
Yuecong Xu, Jianfei Yang, Haozhi Cao, Zhenghua Chen, Qi Li, and Kezhi Mao · 2021
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Sofa: Source-data-free feature alignment for unsupervised domain adaptation
Hao-Wei Yeh, Baoyao Yang, Pong C Yuen, and Tatsuya Harada · 2021
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Logme: Practical assessment of pre-trained models for transfer learning
Kaichao You, Yong Liu, Jianmin Wang, and Mingsheng Long · 2021
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How stable are transferability metrics evaluations?
Andrea Agostinelli, Michal Pándy, Jasper Uijlings, Thomas Mensink, and Vittorio Ferrari · 2022
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Leveraging unlabeled data to predict out-of-distribution performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary C Lipton, Behnam Neyshabur, and Hanie Sedghi · 2022
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Learning transferable parameters for unsupervised domain adaptation
Zhongyi Han, Haoliang Sun, and Yilong Yin · 2022
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Disentangled and side-aware unsupervised domain adaptation for cross-dataset subjective tinnitus diagnosis
Yun Li, Zhe Liu, Lina Yao, Jessica JM Monaghan, and David McAlpine · 2022
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Dine: Domain adaptation from single and multiple black-box predictors
Jian Liang, Dapeng Hu, Jiashi Feng, and Ran He · 2022
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Calibrating class weights with multi-modal information for partial video domain adaptation
Xiyu Wang, Yuecong Xu, Kezhi Mao, and Jianfei Yang · 2022
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A collaborative alignment framework of transferable knowledge extraction for unsupervised domain adaptation
Binhui Xie, Shuang Li, Fangrui Lv, Chi Harold Liu, Guoren Wang, and Dapeng Wu · 2022
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Aligning correlation information for domain adaptation in action recognition
Yuecong Xu, Haozhi Cao, Kezhi Mao, Zhenghua Chen, Lihua Xie, and Jianfei Yang · 2022
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Ranking and tuning pre-trained models: a new paradigm for exploiting model hubs
Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I Jordan, and Mingsheng Long · 2022
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Multi-modal continual test-time adaptation for 3d semantic segmentation
Haozhi Cao, Yuecong Xu, Jianfei Yang, Pengyu Yin, Shenghai Yuan, and Lihua Xie · 2023
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Multi-source video domain adaptation with temporal attentive moment alignment network
Yuecong Xu, Jianfei Yang, Haozhi Cao, Keyu Wu, Min Wu, Zhengguo Li, and Zhenghua Chen · 2023
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