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Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable.
Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky · 1992
Earlier work this paper cites.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
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Semi-supervised classification by low density separation
Olivier Chapelle and Alexander Zien · 2005
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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Tri-training: Exploiting unlabeled data using three classifiers
Zhi-Hua Zhou and Ming Li · 2005
Earlier work this paper cites.
Semi-supervised learning literature survey
Xiaojin Zhu · 2005
Earlier work this paper cites.
Correcting sample selection bias by unlabeled data
Jiayuan Huang, Arthur Gretton, Karsten M Borgwardt, Bernhard Schölkopf, and Alex J Smola · 2007
Earlier work this paper cites.
Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Revisiting natural gradient for deep networks
Razvan Pascanu and Yoshua Bengio · 2013
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Domain adaptation–can quantity compensate for quality?
Shai Ben-David and Ruth Urner · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Training deep neural networks on noisy labels with bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2014
Cited alongside, same era.
From virtual to reality: Fast adaptation of virtual object detectors to real domains
Baochen Sun and Kate Saenko · 2014
Cited alongside, same era.
Virtual and real world adaptation for pedestrian detection
David Vazquez, Antonio M Lopez, Javier Marin, Daniel Ponsa, and David Geronimo · 2014
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Cited alongside, same era.
Learning transferable features with deep adaptation networks
Learning transferrable representations for unsupervised domain adaptation
Ozan Sener, Hyun Oh Song, Ashutosh Saxena, and Silvio Savarese · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
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Good semi-supervised learning that requires a bad gan
Zihang Dai, Zhilin Yang, Fan Yang, William W Cohen, and Ruslan Salakhutdinov · 2017
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Many paths to equilibrium: Gans do not need to decrease adivergence at every step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M Dai, Shakir Mohamed, and Ian Goodfellow · 2017
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Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
Cited alongside, same era.
Optimizing neural networks with kronecker-factored approximate curvature
James Martens and Roger Grosse · 2015
Cited alongside, same era.
Deep reconstruction-classification networks for unsupervised domain adaptation
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li · 2016
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
Cited alongside, same era.
Virtual adversarial training for semi-supervised text classification
Takeru Miyato, Andrew M Dai, and Ian Goodfellow · 2016
Cited alongside, same era.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan
Cited in the paper.
Impossibility theorems for domain adaptation
Shai Ben-David, Tyler Lu, Teresa Luu, and Dávid Pál
Cited in the paper.
Geoffrey French, Michal Mackiewicz, and Mark Fisher · 2017
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2017
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Asymmetric tri-training for unsupervised domain adaptation
Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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A survey on behavior recognition using wifi channel state information
Siamak Yousefi, Hirokazu Narui, Sankalp Dayal, Stefano Ermon, and Shahrokh Valaee · 2017
Later among the works it cites.