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End-to-end learning methods have achieved impressive results in many areas of computer vision.
On information and sufficiency
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Letter to the editor: the kullback-leibler distance
Kullback, S.: · 1987
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Regression shrinkage and selection via the lasso
Tibshirani, R.: · 1996
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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Regularization and variable selection via the elastic net
Zou, H., Hastie, T.: · 2005
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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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Analysis of representations for domain adaptation
Ben-David, S., Blitzer, J., Crammer, K., Pereira, F., et al.: · 2007
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Visualizing data using t-sne
Van der Maaten, L., Hinton, G.: · 2008
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Frustratingly easy domain adaptation
Daumé III, H.: · 2009
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Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: · 2010
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Regularization paths for generalized linear models via coordinate descent
Friedman, J., Hastie, T., Tibshirani, R.: · 2010
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Domain adaptation for object recognition: An unsupervised approach
Gopalan, R., Li, R., Chellappa, R.: · 2011
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Traffic sign recognition with multi-scale convolutional networks
Sermanet, P., LeCun, Y.: · 2011
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Domain adaptation via transfer component analysis
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: · 2011
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Contour detection and hierarchical image segmentation
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Multi-column deep neural network for traffic sign classification
Cireşan, D., Meier, U., Masci, J., Schmidhuber, J.: · 2012
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Unsupervised visual domain adaptation using subspace alignment
Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: · 2013
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Evaluation of traffic sign recognition methods trained on synthetically generated data
Moiseev, B., Konev, A., Chigorin, A., Konushin, A.: · 2013
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Continuous manifold based adaptation for evolving visual domains
Hoffman, J., Darrell, T., Saenko, K.: · 2014
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Domain adaptive neural networks for object recognition
Ghifary, M., Kleijn, W.B., Zhang, M.: · 2014
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Deep domain confusion: Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: · 2014
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Domain-adversarial training of neural networks
Gani, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: · 2015
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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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Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: · 2013
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Selective transfer machine for personalized facial action unit detection
Chu, W.S., Torre, F., Cohn, J.: · 2013
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Transfer sparse coding for robust image representation
Long, M., Ding, G., Wang, J., Sun, J., Guo, Y., Yu, P.: · 2013
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Dlid: Deep learning for domain adaptation by interpolating between domains
Chopra, S., Balakrishnan, S., Gopalan, R.: · 2013
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Long, M., Wang, J.: · 2015
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A deeper look at dataset bias
Tommasi, T., Patricia, N., Caputo, B., Tuytelaars, T.: · 2015
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Visual domain adaptation: A survey of recent advances
Patel, V.M., Gopalan, R., Li, R., Chellappa, R.: · 2015
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Landmarks-based kernelized subspace alignment for unsupervised domain adaptation
Aljundi, R., Emonet, R., Muselet, D., Sebban, M.: · 2015
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Subspace distribution alignment for unsupervised domain adaptation, BMVC (2015)
Sun, B., Saenko, K.: · 2015
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Beyond photo-domain object recognition: Benchmarks for the cross-depiction problem
Cai, H., Wu, Q., Hall, P.: · 2015
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