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Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order to learn a better alignment between the two domains.
Signature verification using a ”siamese” time delay neural network
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: · 1994
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Dimensionality reduction by learning an invariant mapping
Hadsell, R., Chopra, S., LeCun, Y.: · 2006
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Domain adaptation for sentiment classification
Blitzer, J., Dredze, M., Pereira, F.: · 2007
Earlier work this paper cites.
Covariate shift by kernel mean matching
Gretton, A., Smola, A.J., Huang, J., Schmittfull, M., Borgwardt, K.M., Schölkopf, B.: · 2009
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Semi-supervised learning
Chapelle, O., Scholkopf, B., Zien, A.: · 2009
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A survey on transfer learning
Pan, S.J., Yang, Q., et al.: · 2010
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Unbiased look at dataset bias
Torralba, A., Efros, A.A.: · 2011
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The manifold tangent classifier
Rifai, S., Dauphin, Y.N., Vincent, P., Bengio, Y., Muller, X.: · 2011
Earlier work this paper cites.
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
Earlier work this paper cites.
A kernel two-sample test
Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: · 2012
Earlier work this paper cites.
Contrastive learning using spectral methods
Zou, J.Y., Hsu, D.J., Parkes, D.C., Adams, R.P.: · 2013
Earlier work this paper cites.
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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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
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Unsupervised domain adaptation by backpropagation
Ganin, Y., Lempitsky, V.: · 2014
Cited alongside, same era.
Learning fine-grained image similarity with deep ranking
Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu, Y.: · 2014
Cited alongside, same era.
Convolutional neural networks for sentence classification
Kim, Y.: · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Pennington, J., Socher, R., Manning, C.: · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
Cited alongside, same era.
Simultaneous deep transfer across domains and tasks
Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: · 2015
Smooth neighbors on teacher graphs for semi-supervised learning
Luo, Y., Zhu, J., Li, M., Ren, Y., Zhang, B.: · 2017
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Contrastive learning for image captioning
Dai, B., Lin, D.: · 2017
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Triple generative adversarial nets
Li, C., Xu, K., Zhu, J., Zhang, B.: · 2017
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Generalization and equilibrium in generative adversarial nets (GANs)
Arora, S., Ge, R., Liang, Y., Ma, T., Zhang, Y.: · 2017
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Asymmetric tri-training for unsupervised domain adaptation
Saito, K., Ushiku, Y., Harada, T.: · 2017
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A dirt-t approach to unsupervised domain adaptation
Shu, R., Bui, H.H., Narui, H., Ermon, S.: · 2018
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Cited alongside, same era.
Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., Jordan, M.: · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: · 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.
Unsupervised domain adaptation with residual transfer networks
Long, M., Zhu, H., Wang, J., Jordan, M.I.: · 2016
Cited alongside, same era.
Instance normalization: The missing ingredient for fast stylization
Ulyanov, D., Vedaldi, A., Lempitsky, V.: · 2016
Cited alongside, same era.
Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: · 2018
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Co-regularized alignment for unsupervised domain adaptation
Kumar, A., Sattigeri, P., Wadhawan, K., Karlinsky, L., Feris, R., Freeman, B., Wornell, G.: · 2018
Later among the works it cites.
Conditional adversarial domain adaptation
Long, M., CAO, Z., Wang, J., Jordan, M.I.: · 2018
Later among the works it cites.
Multi-adversarial domain adaptation
Pei, Z., Cao, Z., Long, M., Wang, J.: · 2018
Later among the works it cites.
Learning to adapt structured output space for semantic segmentation
Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., Chandraker, M.: · 2018
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Self-ensembling for domain adaptation
French, G., Mackiewicz, M., Fisher, M.: · 2018
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Pivot based language modeling for improved neural domain adaptation
Ziser, Y., Reichart, R.: · 2018
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Adaptive semi-supervised learning for cross-domain sentiment classification
He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: · 2018
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Contrastive adaptation network for unsupervised domain adaptation
Kang, G., Jiang, L., Yang, Y., Hauptmann, A.G.: · 2019
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Unsupervised visual domain adaptation: A deep max-margin gaussian process approach
Kim, M., Sahu, P., Gholami, B., Pavlovic, V.: · 2019
Closest in time.