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Numerous algorithms have been proposed for transferring knowledge from a label-rich domain (source) to a label-scarce domain (target).
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
Earlier work this paper cites.
A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K.M., Rasch, M., Schölkopf, B., Smola, A.J.: · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Maaten, L.v.d., Hinton, G.: · 2008
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.
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
Earlier work this paper cites.
Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., Grauman, K.: · 2012
Earlier work this paper cites.
Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
Gong, B., Grauman, K., Sha, F.: · 2013
Earlier work this paper cites.
Deep domain confusion: Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: · 2014
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
Earlier work this paper cites.
Multi-class open set recognition using probability of inclusion
Jain, L.P., Scheirer, W.J., Boult, T.E.: · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D., Ba, J.: · 2014
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Ganin, Y., Lempitsky, V.: · 2015
Cited alongside, same era.
Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., Jordan, M.I.: · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
Cited alongside, same era.
Unsupervised domain adaptation with residual transfer networks
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: · 2016
Later among the works it cites.
Open set domain adaptation
Busto, P.P., Gall, J.: · 2017
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Asymmetric tri-training for unsupervised domain adaptation
Saito, K., Ushiku, Y., Harada, T.: · 2017
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Unsupervised pixel-level domain adaptation with generative adversarial networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: · 2017
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Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: · 2017
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Adversarial discriminative domain adaptation
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: · 2017
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Long, M., Zhu, H., Wang, J., Jordan, M.I.: · 2016
Cited alongside, same era.
Learning transferrable representations for unsupervised domain adaptation
Sener, O., Song, H.O., Saxena, A., Savarese, S.: · 2016
Cited alongside, same era.
Deep reconstruction-classification networks for unsupervised domain adaptation
Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., Li, W.: · 2016
Cited alongside, same era.
Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
Hoffman, J., Wang, D., Yu, F., Darrell, T.: · 2016
Cited alongside, same era.
Domain separation networks
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: · 2016
Cited alongside, same era.
Unsupervised cross-domain image generation
Taigman, Y., Polyak, A., Wolf, L.: · 2016
Cited alongside, same era.
Towards open set deep networks
Bendale, A., Boult, T.E.: · 2016
Cited alongside, same era.
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Unsupervised image-to-image translation networks
Liu, M.Y., Breuel, T., Kautz, J.: · 2017
Later among the works it cites.
Deep transfer learning with joint adaptation networks
Long, M., Wang, J., Jordan, M.I.: · 2017
Later among the works it cites.
Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation
Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., Zuo, W.: · 2017
Later among the works it cites.
Generative openmax for multi-class open set classification
Ge, Z., Demyanov, S., Chen, Z., Garnavi, R.: · 2017
Later among the works it cites.
Visda: The visual domain adaptation challenge
Peng, X., Usman, B., Kaushik, N., Hoffman, J., Wang, D., Saenko, K.: · 2017
Later among the works it cites.