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While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting.
A theory of the learnable
L. G. Valiant · 1984
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Statistical learning theory , volume 1
V. Vapnik · 1998
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Rademacher penalties and structural risk minimization
V. Koltchinskii · 2001
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Regularized multi–task learning
T. Evgeniou and M. Pontil · 2004
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Detecting change in data streams
D. Kifer, S. Ben-David, and J. Gehrke · 2004
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Domain adaptation with structural correspondence learning
J. Blitzer, R. McDonald, and F. Pereira · 2006
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Correcting sample selection bias by unlabeled data
J. Huang, A. Gretton, K. M. Borgwardt, B. Schölkopf, and A. J. Smola · 2006
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Analysis of representations for domain adaptation
S. Ben-David, J. Blitzer, K. Crammer, F. Pereira, et al · 2007
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Learning bounds for domain adaptation
J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman · 2008
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Sample selection bias correction theory
C. Cortes, M. Mohri, M. Riley, and A. Rostamizadeh · 2008
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Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
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Neural network learning: Theoretical foundations
M. Anthony and P. L. Bartlett · 2009
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Direct density ratio estimation for large-scale covariate shift adaptation
Y. Tsuboi, H. Kashima, S. Hido, S. Bickel, and M. Sugiyama · 2009
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A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
Cited alongside, same era.
A survey on transfer learning
S. J. Pan and Q. Yang · 2010
Cited alongside, same era.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol · 2010
Cited alongside, same era.
Contour detection and hierarchical image segmentation
P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik · 2011
Cited alongside, same era.
Domain adaptation for large-scale sentiment classification: A deep learning approach
X. Glorot, A. Bordes, and Y. Bengio · 2011
Cited alongside, same era.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Non-linear domain adaptation with boosting
C. J. Becker, C. M. Christoudias, and P. Fua · 2013
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A pac-bayesian approach for domain adaptation with specialization to linear classifiers
P. Germain, A. Habrard, F. Laviolette, and E. Morvant · 2013
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Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
B. Gong, K. Grauman, and F. Sha · 2013
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Domain-adversarial neural networks
H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, and M. Marchand · 2014
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Domain adaptation and sample bias correction theory and algorithm for regression
C. Cortes and M. Mohri · 2014
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Cited alongside, same era.
Marginalized denoising autoencoders for domain adaptation
M. Chen, Z. Xu, K. Weinberger, and F. Sha · 2012
Cited alongside, same era.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al · 2012
Cited alongside, same era.
Discovering latent domains for multisource domain adaptation
J. Hoffman, B. Kulis, T. Darrell, and K. Saenko · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Robust domain adaptation
Y. Mansour and M. Schain · 2012
Cited alongside, same era.
Robustness and generalization
H. Xu and S. Mannor · 2012
Cited alongside, same era.
Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. Jordan · 2015
Later among the works it cites.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Domain separation networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
Later among the works it cites.
Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
Later among the works it cites.
Learning from simulated and unsupervised images through adversarial training
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb · 2016
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Understanding traffic density from large-scale web camera data
S. Zhang, G. Wu, J. P. Costeira, and J. M. Moura · 2017
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