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Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain.
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Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
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Lisheng Fu, Thien Huu Nguyen, Bonan Min, and Ralph Grishman · 2017
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Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A Efros, and Trevor Darrell · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Aspect-augmented adversarial networks for domain adaptation
Yuan Zhang, Regina Barzilay, and Tommi Jaakkola · 2017
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Regularized learning for domain adaptation under label shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Animashree Anandkumar · 2018
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Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Domain adaptation with conditional transferable components
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Schölkopf · 2016
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Judy Hoffman, Dequan Wang, Fisher Yu, and Trevor Darrell · 2016
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Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
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Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russ Webb · 2016
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Unsupervised domain adaptation with a relaxed covariate shift assumption
Tameem Adel, Han Zhao, and Alexander Wong · 2017
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Joint distribution optimal transportation for domain adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard, and Alain Rakotomamonjy
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Augmented cyclic adversarial learning for domain adaptation
Ehsan Hosseini-Asl, Yingbo Zhou, Caiming Xiong, and Richard Socher · 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 C Lipton, Yu-Xiang Wang, and Alex Smola · 2018
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Multi-adversarial domain adaptation
Zhongyi Pei, Zhangjie Cao, Mingsheng Long, and Jianmin Wang · 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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Support and invertibility in domain-invariant representations
Fredrik D Johansson, Rajesh Ranganath, and David Sontag · 2019
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