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Semi-supervised Domain Adaptation with Instance Constraints
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
Most successful object classification and detection methods rely on classifiers trained on large labeled datasets. However, for domains where labels are limited, simply borrowing labeled data from existing datasets can hurt performance, a phenomenon known as "dataset bias." We propose a general framework for adapting classifiers from "borrowed" data to the target domain using a combination of available labeled and unlabeled examples. Specifically, we show that imposing smoothness constraints on
doi:10.1109/cvpr.2013.92
dblp:conf/cvpr/DonahueHRSD13
fatcat:ntqkgnsoqrarri4plassqhdsfq