Fetching the paper…
Reading the bibliography…
Unsupervised Domain Adaptation aims to learn a model on a source domain with labeled data in order to perform well on unlabeled data of a target domain.
The estimation of choice probabilities from choice based samples
Manski, Charles F and Lerman, Steven R · 1977
Earlier work this paper cites.
Cost-sensitive learning by cost-proportionate example weighting
Zadrozny, Bianca, Langford, John, and Abe, Naoki · 2003
Earlier work this paper cites.
Discriminative learning for differing training and test distributions
Bickel, Steffen, Brückner, Michael, and Scheffer, Tobias · 2007
Earlier work this paper cites.
Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
Blitzer, John, Dredze, Mark, and Pereira, Fernando · 2007
Earlier work this paper cites.
A kernel method for the two-sample-problem
Gretton, Arthur, Borgwardt, Karsten M, Rasch, Malte, Schölkopf, Bernhard, and Smola, Alex J · 2007
Earlier work this paper cites.
Correcting sample selection bias by unlabeled data
Huang, Jiayuan, Gretton, Arthur, Borgwardt, Karsten M, Schölkopf, Bernhard, and Smola, Alex J · 2007
Earlier work this paper cites.
Covariate shift adaptation by importance weighted cross validation
Sugiyama, Masashi, Krauledat, Matthias, and MÞller, Klaus-Robert · 2007
Earlier work this paper cites.
Direct importance estimation with model selection and its application to covariate shift adaptation
Sugiyama, Masashi, Nakajima, Shinichi, Kashima, Hisashi, Buenau, Paul V, and Kawanabe, Motoaki · 2008
Earlier work this paper cites.
Dataset shift in machine learning, 2009
Candela, J Quiñonero, Sugiyama, Masashi, Schwaighofer, Anton, and Lawrence, Neil D · 2009
Earlier work this paper cites.
When training and test sets are different: characterizing learning transfer
Storkey, Amos · 2009
Earlier work this paper cites.
A survey on transfer learning
Pan, Sinno Jialin, Yang, Qiang, et al · 2010
Earlier work this paper cites.
Cross validation framework to choose amongst models and datasets for transfer learning
Zhong, Erheng, Fan, Wei, Yang, Qiang, Verscheure, Olivier, and Ren, Jiangtao · 2010
Cited alongside, same era.
A kernel two-sample test
Gretton, Arthur, Borgwardt, Karsten M, Rasch, Malte J, Schölkopf, Bernhard, and Smola, Alexander · 2012
Cited alongside, same era.
Unsupervised domain adaptation by domain invariant projection
Baktashmotlagh, Mahsa, Harandi, Mehrtash T, Lovell, Brian C, and Salzmann, Mathieu · 2013
Cited alongside, same era.
Domain adaptation under target and conditional shift
Zhang, Kun, Schölkopf, Bernhard, Muandet, Krikamol, and Wang, Zhikun · 2013
Cited alongside, same era.
A survey on concept drift adaptation
Gama, João, Žliobaitė, Indrė, Bifet, Albert, Pechenizkiy, Mykola, and Bouchachia, Abdelhamid · 2014
Cited alongside, same era.
Domain adaptation with conditional transferable components
Gong, Mingming, Zhang, Kun, Liu, Tongliang, Tao, Dacheng, Glymour, Clark, and Schölkopf, Bernhard · 2016
Later among the works it cites.
Deep transfer learning with joint adaptation networks
Long, Mingsheng, Zhu, Han, Wang, Jianmin, and Jordan, Michael I · 2016
Later among the works it cites.
Arjovsky, Martin, Chintala, Soumith, and Bottou, Léon · 2017
Later among the works it cites.
Joint distribution optimal transportation for domain adaptation
Courty, Nicolas, Flamary, Rémi, Habrard, Amaury, and Rakotomamonjy, Alain · 2017
Later among the works it cites.
Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation
Yan, Hongliang, Ding, Yukang, Li, Peihua, Wang, Qilong, Xu, Yong, and Zuo, Wangmeng · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ganin, Yaroslav and Lempitsky, Victor · 2014
Cited alongside, same era.
Patterns of dataset shift
Kull, Meelis and Flach, Peter · 2014
Cited alongside, same era.
Deep domain confusion: Maximizing for domain invariance
Tzeng, Eric, Hoffman, Judy, Zhang, Ning, Saenko, Kate, and Darrell, Trevor · 2014
Cited alongside, same era.
Robust learning under uncertain test distributions: Relating covariate shift to model misspecification
Wen, Junfeng, Yu, Chun-Nam, and Greiner, Russell · 2014
Cited alongside, same era.
Visual domain adaptation: A survey of recent advances
Patel, Vishal M, Gopalan, Raghuraman, Li, Ruonan, and Chellappa, Rama · 2015
Cited alongside, same era.
Domain-adversarial training of neural networks
Ganin, Yaroslav, Ustinova, Evgeniya, Ajakan, Hana, Germain, Pascal, Larochelle, Hugo, Laviolette, François, Marchand, Mario, and Lempitsky, Victor · 2016
Cited alongside, same era.
Re-weighted adversarial adaptation network for unsupervised domain adaptation
Chen, Qingchao, Liu, Yang, Wang, Zhaowen, Wassell, Ian, and Chetty, Kevin · 2018
Later among the works it cites.
Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation
Damodaran, Bharath Bhushan, Kellenberger, Benjamin, Flamary, Rémi, Tuia, Devis, and Courty, Nicolas · 2018
Later among the works it cites.
Conditional adversarial domain adaptation
Long, Mingsheng, Cao, Zhangjie, Wang, Jianmin, and Jordan, Michael I · 2018
Later among the works it cites.
Simple domain adaptation with class prediction uncertainty alignment
Manders, Jeroen, Marchiori, Elena, and van Laarhoven, Twan · 2018
Later among the works it cites.
Optimal transport for multi-source domain adaptation under target shift
Redko, Ievgen, Courty, Nicolas, Flamary, Rémi, and Tuia, Devis · 2018
Later among the works it cites.
Wasserstein distance guided representation learning for domain adaptation
Shen, Jian, Qu, Yanru, Zhang, Weinan, and Yu, Yong · 2018
Later among the works it cites.