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Unsupervised domain adaptation by domain invariant projection

Baktashmotlagh, Mahsa; Harandi, Mehrtash; Lovell, Brian C; Salzmann, Mathieu

Description

Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be directly suitable for such a comparison, since some of the features may have been distorted by the domain shift, or may be domain specific. In this...[Show more]

CollectionsANU Research Publications
Date published: 2013
Type: Conference paper
URI: http://hdl.handle.net/1885/63543
Source: Proceedings of the IEEE International Conference on Computer Vision
DOI: 10.1109/ICCV.2013.100

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