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Domain adaptation is an important technique to alleviate performance degradation caused by domain shift, e.g., when training and test data come from different domains.
Correcting sample selection bias by unlabeled data
Jiayuan Huang, Arthur Gretton, Karsten M Borgwardt, 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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Practical variational inference for neural networks
Alex Graves · 2011
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Geodesic flow kernel for unsupervised domain adaptation
Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman · 2012
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Cited alongside, same era.
Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Cited alongside, same era.
End-to-end adversarial memory network for cross-domain sentiment classification
Zheng Li, Yun Zhang, Ying Wei, Yuxiang Wu, and Qiang Yang · 2017
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Later among the works it cites.
Re-weighted adversarial adaptation network for unsupervised domain adaptation
Qingchao Chen, Yang Liu, Zhaowen Wang, Ian Wassell, and Kevin Chetty · 2018
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CyCADA: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell · 2018
Later among the works it cites.
Conditional adversarial domain adaptation
Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, and Michael I Jordan · 2018
Later among the works it cites.
Multi-adversarial domain adaptation
Zhongyi Pei, Zhangjie Cao, Mingsheng Long, and Jianmin Wang · 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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Cited alongside, same era.
Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
Cited alongside, same era.
Later among the works it cites.
Exploiting local feature patterns for unsupervised domain adaptation
Jun Wen, Risheng Liu, Nenggan Zheng, Qian Zheng, Zhefeng Gong, and Junsong Yuan · 2019
Closest in time.
Multi-class heterogeneous domain adaptation
Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, and Mingkui Tan · 2019
Closest in time.