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Semi-supervised Domain Adaptation with Subspace Learning for visual recognition
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
In many real-world applications, we are often facing the problem of cross domain learning, i.e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain. However, simply applying existing source data or knowledge may even hurt the performance, especially when the data distribution in the source and target domain is quite different, or there are very few labeled data available in the target domain. This paper proposes a novel domain adaptation
doi:10.1109/cvpr.2015.7298826
dblp:conf/cvpr/YaoPNLM15
fatcat:uxgbcxd2kfagtj5nknmsmuo234