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This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster.
Some methods for classification and analysis of multivariate observations
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The mnist database of handwritten digits
Yann LeCun · 1998
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Cluster ensembles—a knowledge reuse framework for combining multiple partitions
Alexander Strehl and Joydeep Ghosh · 2002
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Distance metric learning with application to clustering with side-information
Eric P Xing, Michael I Jordan, Stuart J Russell, and Andrew Y Ng · 2003
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Integrating constraints and metric learning in semi-supervised clustering
Mikhail Bilenko, Sugato Basu, and Raymond J Mooney · 2004
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A survey of clustering with instance level
Ian Davidson and Sugato Basu · 2007
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Information-theoretic metric learning
Jason V Davis, Brian Kulis, Prateek Jain, Suvrit Sra, and Inderjit S Dhillon · 2007
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Locality preserving nonnegative matrix factorization
Deng Cai, Xiaofei He, Xuanhui Wang, Hujun Bao, and Jiawei Han · 2009
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Translated learning: Transfer learning across different feature spaces
Wenyuan Dai, Yuqiang Chen, Gui rong Xue, Qiang Yang, and Yong Yu · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Adapting Visual Category Models to New Domains , pp. 213–226
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Image clustering using local discriminant models and global integration
Yi Yang, Dong Xu, Feiping Nie, Shuicheng Yan, and Yueting Zhuang · 2010
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Large scale spectral clustering with landmark-based representation
Xinlei Chen and Deng Cai · 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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Cecm: Constrained evidential c-means algorithm
Violaine Antoine, Benjamin Quost, M-H Masson, and Thierry Denoeux · 2012
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Constrained 1-spectral clustering
Syama Sundar Rangapuram and Matthias Hein · 2012
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Semi-supervised kernel mean shift clustering
Saket Anand, Sushil Mittal, Oncel Tuzel, and Peter Meer · 2014
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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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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 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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On constrained spectral clustering and its applications
A survey of constrained clustering
Derya Dinler and Mustafa Kemal Tural · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Neural network-based clustering using pairwise constraints
Yen-Chang Hsu and Zsolt Kira · 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 transferrable representations for unsupervised domain adaptation
Ozan Sener, Hyun Oh Song, Ashutosh Saxena, and Silvio Savarese · 2016
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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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Sergey Ioffe and Christian Szegedy · 2015
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Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 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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Autodial: Automatic domain alignment layers
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Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I. Jordan · 2017
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Asymmetric tri-training for unsupervised domain adaptation
Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 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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