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Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Covariate shift and local learning by distribution matching
A. Gretton, AJ. Smola, J. Huang, M. Schmittfull, KM. Borgwardt, and B. Schölkopf · 2009
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Unbiased look at dataset bias
Antonio Torralba and Alexei A. Efros · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Transfer feature learning with joint distribution adaptation
M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
Cited alongside, same era.
Learning rich features from rgb-d images for object detection and segmentation
Saurabh Gupta, Ross Girshick, Pablo Arbeláez, and Jitendra Malik · 2014
Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
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Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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Deep CORAL: correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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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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Coupled generative adversarial networks
Ming-Yu Liu and Oncel Tuzel · 2016
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Learning transferable features with deep adaptation networks
Mingsheng Long and Jianmin Wang · 2015
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Cited alongside, same era.
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 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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Beyond sharing weights for deep domain adaptation
Artem Rozantsev, Mathieu Salzmann, and Pascal Fua · 2016
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