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The empirical fact that classifiers, trained on given data collections, perform poorly when tested on data acquired in different settings is theoretically explained in domain adaptation through a shift among distributions of the source and target domains.
Semi-supervised learning by entropy minimization
Grandvalet, Y., Bengio, Y.: · 2004
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Correcting sample selection bias by unlabeled data
Huang, J., Gretton, A., Borgwardt, K.M., Schölkopf, B., Smola, A.J.: · 2006
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Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: · 2010
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Impossibility theorems for domain adaptation
Ben-David, S., Lu, T., Luu, T., Pál, D.: · 2010
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No bias left behind: Covariate shift adaptation for discriminative 3d pose estimation
Yamada, M., Sigal, L., Raptis, M.: · 2012
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Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., Grauman, K.: · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Selective transfer machine for personalized facial action unit detection
Chu, W.S., De la Torre, F., Cohn, J.F.: · 2013
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Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
Gong, B., Grauman, K., Sha, F.: · 2013
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A transfer learning approach for multi-cue semantic place recognition
Costante, G., Ciarfuglia, T.A., Valigi, P., Ricci, E.: · 2013
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Transfer sparse coding for robust image representation
Long, M., Ding, G., Wang, J., Sun, J., Guo, Y., Yu, P.S.: · 2013
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Unsupervised visual domain adaptation using subspace alignment
Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: · 2013
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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., Darrell, T.: · 2014
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Deep learning of scene-specific classifier for pedestrian detection
Zeng, X., Ouyang, W., Wang, M., Wang, X.: · 2014
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Unsupervised domain adaptation for personalized facial emotion recognition
Zen, G., Sangineto, E., Ricci, E., Sebe, N.: · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Unsupervised domain adaptation with residual transfer networks
Long, M., Wang, J., Jordan, M.I.: · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Sun, B., Saenko, K.: · 2016
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Deep reconstruction-classification networks for unsupervised domain adaptation
Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., Li, W.: · 2016
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Domain separation networks
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: · 2016
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Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: · 2014
Cited alongside, same era.
Learning transferable features with deep adaptation networks
Long, M., Wang, J.: · 2015
Cited alongside, same era.
Simultaneous deep transfer across domains and tasks
Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: · 2015
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Ganin, Y., Lempitsky, V.: · 2015
Cited alongside, same era.
Li, Y., Wang, N., Shi, J., Liu, J., Hou, X.: · 2016
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Beyond sharing weights for deep domain adaptation
Rozantsev, A., Salzmann, M., Fua, P.: · 2016
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Return of frustratingly easy domain adaptation
Sun, B., Feng, J., Saenko, K.: · 2016
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Normalizing the normalizers: Comparing and extending network normalization schemes
Ren, M., Liao, R., Urtasun, R., Sinz, F.H., Zemel, R.S.: · 2016
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