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Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain.
Absolute values of the coefficients of the polynomials in weierstrass’s approximation theorem
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Gradient-based learning applied to document recognition
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Analysis of representations for domain adaptation
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A kernel method for the two-sample-problem
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Learning from multiple sources
Koby Crammer, Michael Kearns, and Jennifer Wortman · 2008
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Domain adaptation with multiple sources
Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh, and A R · 2009
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Dataset Shift in Machine Learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence · 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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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 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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Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach
Lixin Duan, Dong Xu, and Shih-Fu Chang · 2012
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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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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Domain adaptive neural networks for object recognition
Muhammad Ghifary, W Bastiaan Kleijn, and Mengjie Zhang · 2014
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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Generative moment matching networks
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Learning transferable features with deep adaptation networks
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Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
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Adversarial discriminative domain adaptation
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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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Dualgan: Unsupervised dual learning for image-to-image translation
Zili Yi, Hao (Richard) Zhang, Ping Tan, and Minglun Gong · 2017
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Central moment discrepancy (CMD) for domain-invariant representation learning
Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz · 2017
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Deep reconstruction-classification networks for unsupervised domain adaptation
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Coupled generative adversarial networks
Ming-Yu Liu and Oncel Tuzel · 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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Return of frustratingly easy domain adaptation
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Learning to discover cross-domain relations with generative adversarial networks
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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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Self-ensembling for visual domain adaptation
Geoff French, Michal Mackiewicz, and Mark Fisher · 2018
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Algorithms and theory for multiple-source adaptation
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CyCADA: Cycle-consistent adversarial domain adaptation
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Museum exhibit identification challenge for the supervised domain adaptation and beyond
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Maximum classifier discrepancy for unsupervised domain adaptation
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Visual domain adaptation with manifold embedded distribution alignment
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Deep cocktail network: Multi-source unsupervised domain adaptation with category shift
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Aligning infinite-dimensional covariance matrices in reproducing kernel hilbert spaces for domain adaptation
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Adversarial multiple source domain adaptation
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