Semi-supervised Domain Adaptation with Subspace Learning for visual recognition

Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, Tao Mei
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
more » ... ramework, named Semi-supervised Domain Adaptation with Subspace Learning (SDASL), which jointly explores invariant lowdimensional structures across domains to correct data distribution mismatch and leverages available unlabeled target examples to exploit the underlying intrinsic information in the target domain. Specifically, SDASL conducts the learning by simultaneously minimizing the classification error, preserving the structure within and across domains, and restricting similarity defined on unlabeled target examples. Encouraging results are reported for two challenging domain transfer tasks (including image-to-image and imageto-video transfers) on several standard datasets in the context of both image object recognition and video concept detection.
doi:10.1109/cvpr.2015.7298826 dblp:conf/cvpr/YaoPNLM15 fatcat:uxgbcxd2kfagtj5nknmsmuo234