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Unsupervised Domain Adaptation (DA) is used to automatize the task of labeling data: an unlabeled dataset (target) is annotated using a labeled dataset (source) from a related domain.
An introduction to support vector machines and other kernel-based learning methods
Nello Cristianini and John Shawe-Taylor · 2000
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A generalized representer theorem
Bernhard Schölkopf, Ralf Herbrich, and Alex J Smola · 2001
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Partially labeled classification with markov random walks
Martin Szummer and Tommi Jaakkola · 2002
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Learning from labeled and unlabeled data on a directed graph
Dengyong Zhou, Jiayuan Huang, and Bernhard Schölkopf · 2005
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Large scale transductive svms
Ronan Collobert, Fabian Sinz, Jason Weston, and Léon Bottou · 2006
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, Fernando Pereira, et al · 2007
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Optimization techniques for semi-supervised support vector machines
Olivier Chapelle, Vikas Sindhwani, and Sathiya S Keerthi · 2008
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Liblinear: A library for large linear classification
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin · 2008
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A self-training semi-supervised svm algorithm and its application in an eeg-based brain computer interface speller system
Yuanqing Li, Cuntai Guan, Huiqi Li, and Zhengyang Chin · 2008
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Domain adaptation problems: A dasvm classification technique and a circular validation strategy
Lorenzo Bruzzone and Mattia Marconcini · 2010
Cited alongside, same era.
Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
Cited alongside, same era.
A literature review of domain adaptation with unlabeled data
Anna Margolis · 2011
Cited alongside, same era.
Geodesic flow kernel for unsupervised domain adaptation
Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman · 2012
Cited alongside, same era.
Unsupervised visual domain adaptation using subspace alignment
Basura Fernando, Amaury Habrard, Marc Sebban, and Tinne Tuytelaars · 2013
Cited alongside, same era.
Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep transfer learning with joint adaptation networks
Mingsheng Long, Jianmin Wang, and Michael I Jordan · 2016
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Unsupervised domain adaptation with residual transfer networks
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Boqing Gong, Kristen Grauman, and Fei Sha · 2013
Cited alongside, same era.
A testbed for cross-dataset analysis
Tatiana Tommasi and Tinne Tuytelaars · 2014
Cited alongside, same era.
Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
Cited alongside, same era.
Mingsheng Long, Jianmin Wang, and Michael I Jordan · 2016
Later among the works it cites.
Learning transferrable representations for unsupervised domain adaptation
Ozan Sener, Hyun Oh Song, Ashutosh Saxena, and Silvio Savarese · 2016
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Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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