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Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input distributions.
Correcting sample selection bias by unlabeled data
Huang, J., Smola, A.J., Gretton, A., Borgwardt, K.M., Schölkopf, B.: · 2006
Earlier work this paper cites.
Instance Weighting for Domain Adaptation in NLP
Jiang, J., Zhai, C.: · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
Earlier work this paper cites.
Domain adaptation via transfer component analysis
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: · 2009
Earlier work this paper cites.
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: · 2010
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Domain adaptation for object recognition: An unsupervised approach
Gopalan, R., Li, R., Chellappa, R.: · 2011
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Learning from multiple outlooks
Harel, M., Mannor, S.: · 2011
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Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., Grauman, K.: · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
Cited alongside, same era.
Unsupervised visual domain adaptation using subspace alignment
Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: · 2013
Cited alongside, same era.
Dlid: Deep learning for domain adaptation by interpolating between domains
Chopra, S., Balakrishnan, S., Gopalan, R.: · 2013
Cited alongside, same era.
Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: · 2014
Cited alongside, same era.
From virtual to reality: Fast adaptation of virtual object detectors to real domains
Sun, B., Saenko, K.: · 2014
Cited alongside, same era.
What do deep cnns learn about objects?
Peng, X., Sun, B., Ali, K., Saenko, K.: · 2015
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Learning deep object detectors from 3d models
Peng, X., Sun, B., Ali, K., Saenko, K.: · 2015
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Generating large scale image datasets from 3d cad models
Sun, B., Peng, X., Saenko, K.: · 2015
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Subspace distribution alignment for unsupervised domain adaptation
Sun, B., Saenko, K.: · 2015
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Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., Jordan, M.I.: · 2015
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Unsupervised domain adaptation by backpropagation
Ganin, Y., Lempitsky, V.: · 2015
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Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
Cited alongside, same era.
Return of frustratingly easy domain adaptation
Sun, B., Feng, J., Saenko, K.: · 2016
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