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Appropriately evaluating the discrepancy between domains is essential for the success of unsupervised domain adaptation.
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Unlabeled data can degrade classification performance of generative classifiers
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A new analysis of the value of unlabeled data in semi-supervised learning for image retrieval
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Semi-Supervised Learning
Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien · 2006
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2007
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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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Instance weighting for domain adaptation in NLP
Jing Jiang and ChengXiang Zhai · 2007
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Covariate shift adaptation by importance weighted cross validation
Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert Müller · 2007
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Does unlabeled data provably help? worst-case analysis of the sample complexity of semi-supervised learning
Shai Ben-David, Tyler Lu, and Dávid Pál · 2008
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Direct importance estimation with model selection and its application to covariate shift adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul V Buenau, and Motoaki Kawanabe · 2008
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Aarti Singh, Robert Nowak, and Jerry Zhu · 2009
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2012
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Generalization bounds for domain adaptation
Chao Zhang, Lei Zhang, and Jieping Ye · 2012
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Unlabeled data does provably help
Malte Darnstädt, Hans Ulrich Simon, and Balázs Szörényi · 2013
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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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Mastering the game of go with deep neural networks and tree search
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Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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Unsupervised pixel-level domain adaptation with generative adversarial networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2017
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Pascal Germain, Amaury Habrard, François Laviolette, and Emilie Morvant · 2013
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Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, and Andrew Y Ng · 2013
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Autoencoder-based unsupervised domain adaptation for speech emotion recognition
Jun Deng, Zixing Zhang, Florian Eyben, and Björn Schuller · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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The CIFAR-10 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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Joint distribution optimal transportation for domain adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard, and Alain Rakotomamonjy · 2017
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Robust semi-supervised least squares classification by implicit constraints
Jesse H Krijthe and Marco Loog · 2017
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Theoretical analysis of domain adaptation with optimal transport
Ievgen Redko, Amaury Habrard, and Marc Sebban · 2017
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Asymmetric tri-training for unsupervised domain adaptation
Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada · 2017
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Semi-supervised classification based on classification from positive and unlabeled data
Tomoya Sakai, Marthinus Christoffel Plessis, Gang Niu, and Masashi Sugiyama · 2017
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
T. Miyato, S. Maeda, S. Ishii, and M. Koyama · 2018
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On the convergence of adam and beyond
Sashank J Reddi, Satyen Kale, and Sanjiv Kumar · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 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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Adaptation based on generalized discrepancy
Corinna Cortes, Mehryar Mohri, and Andrés Munoz Medina · 2019
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Unsupervised domain adaptation based on source-guided discrepancy
Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, and Masashi Sugiyama · 2019
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