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Distribution-Matching Embedding for Visual Domain Adaptation

Baktashmotlagh, Mahsa; Harandi, M; Salzmann, M


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...[Show more]

CollectionsANU Research Publications
Date published: 2016
Type: Journal article
Source: Journal of Machine Learning Research


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