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Domain adaptation techniques aim at adapting a classifier learnt on a source domain to work on the target domain.
Leggetter, Christopher J., and Philip C. Woodland. ”Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models.” Computer Speech & Language 9.2 (1995): 171-185
1995
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Ben-David, Shai, John Blitzer, Koby Crammer, and Fernando Pereira. ”Analysis of representations for domain adaptation.” Advances in neural information processing systems 19 (2007): 137
2007
Earlier work this paper cites.
Griffin, Gregory, Alex Holub, and Pietro Perona. ”Caltech-256 object category dataset.” (2007)
2007
Earlier work this paper cites.
Finkel, Jenny Rose, and Christopher D. Manning. ”Hierarchical bayesian domain adaptation.” Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2009
2009
Earlier work this paper cites.
Uribe, Diego. ”Domain adaptation in sentiment classification.” In Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on, pp. 857-860. IEEE, 2010
2010
Earlier work this paper cites.
Perronnin, Florent, Jorge Sánchez, and Yan Liu. ”Large-scale image categorization with explicit data embedding.” In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 2297-2304. IEEE, 2010
2010
Earlier work this paper cites.
2010
Cited alongside, same era.
Gopalan, Raghuraman, Ruonan Li, and Rama Chellappa. ”Domain adaptation for object recognition: An unsupervised approach.” In Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 999-1006. IEEE, 2011
2011
Cited alongside, same era.
Torralba, Antonio, and Alexei A. Efros. ”Unbiased look at dataset bias.” In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 1521-1528. IEEE, 2011
2011
Cited alongside, same era.
Margolis, Anna. ”A literature review of domain adaptation with unlabeled data.” Tec. Report (2011): 1-42
2011
Cited alongside, same era.
Khosla, Aditya, Tinghui Zhou, Tomasz Malisiewicz, Alexei A. Efros, and Antonio Torralba. ”Undoing the damage of dataset bias.” In Computer Vision–ECCV 2012, pp. 158-171. Springer Berlin Heidelberg, 2012
2012
Later among the works it cites.
Fernando, Basura, Amaury Habrard, Marc Sebban, and Tinne Tuytelaars. ”Unsupervised visual domain adaptation using subspace alignment.” In Computer Vision (ICCV), 2013 IEEE International Conference on, pp. 2960-2967. IEEE, 2013
2013
Later among the works it cites.
Nguyen, Hien V., Huy Tho Ho, Vishal M. Patel, and Rama Chellappa. ”Joint hierarchical domain adaptation and feature learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, submitted (2013)
2013
Later among the works it cites.
2014
Later among the works it cites.
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Glorot, Xavier, Antoine Bordes, and Yoshua Bengio. ”Domain adaptation for large-scale sentiment classification: A deep learning approach.” In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 513-520. 2011
2011
Cited alongside, same era.
Gong, Boqing, Yuan Shi, Fei Sha, and Kristen Grauman. ”Geodesic flow kernel for unsupervised domain adaptation.” In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 2066-2073. IEEE, 2012
2012
Cited alongside, same era.
Patel, Vishal M., Raghuraman Gopalan, Ruonan Li, and Rama Chellappa. ”Visual Domain Adaptation: An Overview of Recent Advances.”
Cited in the paper.
Donahue, Jeff, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. ”DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.” In Proceedings of The 31st International Conference on Machine Learning, pp. 647-655. 2014
2014
Later among the works it cites.