Fetching the paper…
Reading the bibliography…
This work provides a framework for addressing the problem of supervised domain adaptation with deep models.
On information and sufficiency
S. Kullback and R. A. Leibler · 1951
Earlier work this paper cites.
A database for handwritten text recognition research
J. J. Hull · 1994
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Improving predictive inference under covariate shift by weighting the log-likelihood function
H. Shimodaira · 2000
Earlier work this paper cites.
Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
Earlier work this paper cites.
Domain adaptation with structural correspondence learning
J. Blitzer, R. McDonald, and F. Pereira · 2006
Earlier work this paper cites.
Domain adaptation for statistical classifiers
H. Daume III and D. Marcu · 2006
Earlier work this paper cites.
A kernel method for the two-sample-problem
A. Gretton, K. M. Borgwardt, M. Rasch, B. Schölkopf, and A. J. Smola · 2006
Earlier work this paper cites.
Dataset issues in object recognition
J. Ponce, T. L. Berg, M. Everingham, D. A. Forsyth, M. Hebert, S. Lazebnik, M. Marszalek, C. Schmid, B. C. Russell, A. Torralba, et al · 2006
Earlier work this paper cites.
Adapting svm classifiers to data with shifted distributions
J. Yang, R. Yan, and A. G. Hauptmann · 2007
Earlier work this paper cites.
Domain transfer svm for video concept detection
L. Duan, I. W. Tsang, D. Xu, and S. J. Maybank · 2009
Earlier work this paper cites.
Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach
A. Bergamo and L. Torresani · 2010
Earlier work this paper cites.
Adapting visual category models to new domains
K. Saenko, B. Kulis, M. Fritz, and T. Darrell · 2010
Earlier work this paper cites.
Tabula rasa: Model transfer for object category detection
Y. Aytar and A. Zisserman · 2011
Earlier work this paper cites.
Domain adaptation for object recognition: An unsupervised approach
R. Gopalan, R. Li, and R. Chellappa · 2011
Earlier work this paper cites.
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
B. Kulis, K. Saenko, and T. Darrell · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
Domain adaptation via transfer component analysis
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang · 2011
Earlier work this paper cites.
Unbiased look at dataset bias
A. Torralba and A. A. Efros · 2011
Earlier work this paper cites.
Geodesic flow kernel for unsupervised domain adaptation
B. Gong, Y. Shi, F. Sha, and K. Grauman · 2012
Earlier work this paper cites.
Cross language text classification via subspace co-regularized multi-view learning
Y. Guo and M. Xiao · 2012
Earlier work this paper cites.
Unsupervised domain adaptation by domain invariant projection
M. Baktashmotlagh, M. T. Harandi, B. C. Lovell, and M. Salzmann · 2013
Earlier work this paper cites.
Non-linear domain adaptation with boosting
C. J. Becker, C. M. Christoudias, and P. Fua · 2013
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 · 2013
Cited alongside, same era.
Unsupervised visual domain adaptation using subspace alignment
B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars · 2013
Cited alongside, same era.
Transfer sparse coding for robust image representation
M. Long, G. Ding, J. Wang, J. Sun, Y. Guo, and P. S. Yu · 2013
Cited alongside, same era.
Domain generalization via invariant feature representation
K. Muandet, D. Balduzzi, and B. Schölkopf · 2013
Cited alongside, same era.
Recognizing RGB images by learning from RGB-D data
L. Chen, W. Li, and D. Xu · 2014
Deep reconstruction-classification networks for unsupervised domain adaptation
M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi, and W. Li · 2016
Later among the works it cites.
Nips 2016 tutorial: Generative adversarial networks
I. Goodfellow · 2016
Later among the works it cites.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2016
Later among the works it cites.
Domain adaptation by mixture of alignments of second-or higher-order scatter tensors
P. Koniusz, Y. Tas, and F. Porikli · 2016
Later among the works it cites.
Learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions
B. Kumar, G. Carneiro, I. Reid, et al · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2014
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Discriminative deep metric learning for face verification in the wild
J. Hu, J. Lu, and Y.-P. Tan · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
Learning using privileged information: SVM+ and weighted SVM
M. Lapin, M. Hein, and B. Schiele · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Later among the works it cites.
Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
Later among the works it cites.
Information bottleneck domain adaptation with privileged information for visual recognition
S. Motiian and G. Doretto · 2016
Later among the works it cites.
Information bottleneck learning using privileged information for visual recognition
S. Motiian, M. Piccirilli, D. A. Adjeroh, and G. Doretto · 2016
Later among the works it cites.
Generative adversarial text to image synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee · 2016
Later among the works it cites.
Learning what and where to draw
S. E. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee · 2016
Later among the works it cites.
Beyond sharing weights for deep domain adaptation
A. Rozantsev, M. Salzmann, and P. Fua · 2016
Later among the works it cites.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Later among the works it cites.
Deep coral: Correlation alignment for deep domain adaptation
B. Sun and K. Saenko · 2016
Later among the works it cites.
Learning the roots of visual domain shift
T. Tommasi, M. Lanzi, P. Russo, and B. Caputo · 2016
Later among the works it cites.
A siamese long short-term memory architecture for human re-identification
R. R. Varior, B. Shuai, J. Lu, D. Xu, and G. Wang · 2016
Later among the works it cites.
Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
H. Zhang, T. Xu, H. Li, S. Zhang, X. Huang, X. Wang, and D. Metaxas · 2016
Later among the works it cites.
Unified deep supervised domain adaptation and generalization
S. Motiian, M. Piccirilli, D. A. Adjeroh, and G. Doretto · 2017
Closest in time.
Generate to adapt: Aligning domains using generative adversarial networks
S. Sankaranarayanan, Y. Balaji, C. D. Castillo, and R. Chellappa · 2017
Closest in time.
Adaptive svm+: Learning with privileged information for domain adaptation
N. Sarafianos, M. Vrigkas, and I. A. Kakadiaris · 2017
Closest in time.
Learning from simulated and unsupervised images through adversarial training
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb · 2017
Closest in time.
Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
Closest in time.