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In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted as a discrepancy metric between the distributions of source and target domains.
A classification em algorithm for clustering and two stochastic versions
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Integrating structured biological data by kernel maximum mean discrepancy
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A kernel method for the two-sample-problem
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Correcting sample selection bias by unlabeled data
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Caltech-256 object category dataset
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Cross domain distribution adaptation via kernel mapping
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The pascal visual object classes (voc) challenge
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A survey on transfer learning
S. J. Pan and Q. Yang · 2010
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Adapting visual category models to new domains
K. Saenko, B. Kulis, M. Fritz, and T. Darrell · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba · 2010
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Domain adaptation via transfer component analysis
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang · 2011
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Geodesic flow kernel for unsupervised domain adaptation
B. Gong, Y. Shi, F. Sha, and K. Grauman · 2012
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A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
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Optimal kernel choice for large-scale two-sample tests
A. Gretton, D. Sejdinovic, H. Strathmann, S. Balakrishnan, M. Pontil, K. Fukumizu, and B. K. Sriperumbudur · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Deep learning via semi-supervised embedding
J. Weston, F. Ratle, H. Mobahi, and R. Collobert · 2012
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Unsupervised visual domain adaptation using subspace alignment
B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars · 2013
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 2014
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Accelerating t-sne using tree-based algorithms
L. Van Der Maaten · 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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From generic to specific deep representations for visual recognition
H. Azizpour, A. Sharif Razavian, J. Sullivan, A. Maki, and S. Carlsson · 2015
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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
Cited alongside, same era.
Transfer feature learning with joint distribution adaptation
M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu · 2013
Cited alongside, same era.
Semi-supervised learning and domain adaptation in natural language processing
A. Søgaard · 2013
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Overview of the imageclef 2014 domain adaptation task
B. Caputo and N. Patricia · 2014
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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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2014
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Y. Li, K. Swersky, and R. Zemel · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning transferable features with deep adaptation networks
M. Long and J. Wang · 2015
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Unsupervised domain adaptation with imbalanced cross-domain data
T. Ming Harry Hsu, W. Yu Chen, C.-A. Hou, Y.-H. Hubert Tsai, Y.-R. Yeh, and Y.-C. Frank Wang · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Simultaneous deep transfer across domains and tasks
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko · 2015
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Distribution-matching embedding for visual domain adaptation
M. Baktashmotlagh, M. T. Harandi, and M. Salzmann · 2016
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Unsupervised domain adaptation with residual transfer networks
M. Long, J. Wang, and M. I. Jordan · 2016
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