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Adversarial learning has demonstrated good performance in the unsupervised domain adaptation setting, by learning domain-invariant representations.
Batch weight for domain adaptation with mass shift
Mikolaj Binkowski, R. Devon Hjelm, and Aaron C. Courville · 1905
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Robust natural language inference models with example forgetting
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Dominik Maria Endres and Johannes E Schindelin · 2003
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Word sense disambiguation with distribution estimation
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Correcting sample selection bias by unlabeled data
Jiayuan Huang, Arthur Gretton, Karsten M Borgwardt, Bernhard Schölkopf, and Alex J Smola · 2006
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira, et al · 2007
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Properties of classical and quantum jensen-shannon divergence
Jop Briët and Peter Harremoës · 2009
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Frustratingly easy domain adaptation
Hal Daumé III · 2009
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Covariate shift by kernel mean matching
Arthur Gretton, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, and Bernhard Schölkopf · 2009
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Domain adaptation with multiple sources
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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When training and test sets are different: Characterising learning transfer
Amos Storkey · 2009
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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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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 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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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris M. Mooij · 2012
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Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
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Semi-supervised learning of class balance under class-prior change by distribution matching
Marthinus Christoffel du Plessis and Masashi Sugiyama · 2014
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Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Transfer joint matching for unsupervised domain adaptation
Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, and Philip S Yu · 2014
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 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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Visual domain adaptation challenge, 2017
Visda · 2017
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Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation
Hongliang Yan, Yukang Ding, Peihua Li, Qilong Wang, Yong Xu, and Wangmeng Zuo · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2017
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Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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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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Continuous target shift adaptation in supervised learning
Tuan Duong Nguyen, Marthinus Christoffel du Plessis, and Masashi Sugiyama · 2015
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Deep transfer network: Unsupervised domain adaptation
Xu Zhang, Felix X. Yu, Shih-Fu Chang, and Shengjin Wang · 2015
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Unsupervised domain adaptation using approximate label matching
Jordan T Ash, Robert E Schapire, and Barbara E Engelhardt · 2016
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Domain separation networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan · 2016
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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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Qingchao Chen, Yang Liu, Zhaowen Wang, Ian Wassell, and Kevin Chetty · 2018
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Multi-source domain adaptation with mixture of experts
Jiang Guo, Darsh J Shah, and Regina Barzilay · 2018
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Minimax statistical learning with wasserstein distances
Jaeho Lee and Maxim Raginsky · 2018
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Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I. Jordan · 2018
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Wasserstein distance guided representation learning for domain adaptation
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu · 2018
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Learning semantic representations for unsupervised domain adaptation
Shaoan Xie, Zibin Zheng, Liang Chen, and Chuan Chen · 2018
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Importance weighted adversarial nets for partial domain adaptation
Jing Zhang, Zewei Ding, Wanqing Li, and Philip Ogunbona · 2018
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Regularized learning for domain adaptation under label shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Animashree Anandkumar · 2019
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Learning to transfer examples for partial domain adaptation
Zhangjie Cao, Kaichao You, Mingsheng Long, Jianmin Wang, and Qiang Yang · 2019
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Transferable adversarial training: A general approach to adapting deep classifiers
Hong Liu, Mingsheng Long, Jianmin Wang, and Michael I. Jordan · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
R. Thomas McCoy, Ellie Pavlick, and Tal Linzen · 2019
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Domain agnostic learning with disentangled representations
Xingchao Peng, Zijun Huang, Ximeng Sun, and Kate Saenko · 2019
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Optimal transport for multi-source domain adaptation under target shift
Ievgen Redko, Nicolas Courty, Rémi Flamary, and Devis Tuia · 2019
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