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Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation.
Analysis of representations for domain adaptation
Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F · 2007
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Caltech-256 object category dataset
Griffin, G., Holub, A., and Perona, P · 2007
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Deep learning via semi-supervised embedding
Weston, J., Rattle, F., and Collobert, R · 2008
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Domain adaptation: Learning bounds and algorithms
Mansour, Y., Mohri, M., and Rostamizadeh, A · 2009
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Kernel choice and classifiability for rkhs embeddings of probability distributions
Sriperumbudur, B. K., Fukumizu, K., Gretton, A., Lanckriet, G., and Schölkopf, B · 2009
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A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. W · 2010
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A survey on transfer learning
Pan, S. J. and Yang, Q · 2010
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Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., and Darrell, T · 2010
Earlier work this paper cites.
Domain adaptation for large-scale sentiment classification: A deep learning approach
Glorot, X., Bordes, A., and Bengio, Y · 2011
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Multimodal deep learning
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., and Ng, A. Y · 2011
Earlier work this paper cites.
Domain adaptation via transfer component analysis
Pan, S. J., Tsang, I. W., Kwok, J. T., and Yang, Q · 2011
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Unbiased look at dataset bias
Torralba, A. and Efros, A. A · 2011
Cited alongside, same era.
Marginalized denoising autoencoders for domain adaptation
Chen, M., Xu, Z., Weinberger, K. Q., and Sha, F · 2012
Cited alongside, same era.
Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., and Grauman, K · 2012
Cited alongside, same era.
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Cited alongside, same era.
Unsupervised domain adaptation by domain invariant projection
Baktashmotlagh, M., Harandi, M. T., Lovell, B. C., and Salzmann, M · 2013
Domain adaptation under target and conditional shift
Zhang, K., Schölkopf, B., Muandet, K., and Wang, Z · 2013
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Domain-adversarial neural networks
Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., and Marchand, M · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T · 2014
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Domain adaptive neural networks for object recognition
Ghifary, M., Kleijn, W. B., and Zhang, M · 2014
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LSDA: Large scale detection through adaptation
Hoffman, J., Guadarrama, S., Tzeng, E., Hu, R., Donahue, J., Girshick, R., Darrell, T., and Saenko, K · 2014
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Caffe: Convolutional architecture for fast feature embedding
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Cited alongside, same era.
Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
Cited alongside, same era.
Multi-source deep learning for information trustworthiness estimation
Ge, L., Gao, J., Li, X., and Zhang, A · 2013
Cited alongside, same era.
Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
Gong, B., Grauman, K., and Sha, F · 2013
Cited alongside, same era.
Transfer feature learning with joint distribution adaptation
Long, M., Wang, J., Ding, G., Sun, J., and Yu, P. S · 2013
Cited alongside, same era.
Equivalence of distance-based and rkhs-based statistics in hypothesis testing
Sejdinovic, D., Sriperumbudur, B., Gretton, A., and Fukumizu, K · 2013
Cited alongside, same era.
A kernel two-sample test
Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., and Smola, A
Cited in the paper.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T · 2014
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2014
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Deep domain confusion: Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., and Darrell, T · 2014
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Flexible transfer learning under support and model shift
Wang, X. and Schneider, J · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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