Unsupervised Domain Adaptation by Domain Invariant Projection

Baktashmotlagh, M., Harandi, Mehrtash T., Lovell, Brian C., & Salzmann, M. (2013) Unsupervised Domain Adaptation by Domain Invariant Projection. In International Conference in Computer Vision (ICCV), December 1-8, 2013, Sydney Convention and Exhibition Centre.

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Abstract

Domain-invariant representations are key to addressing the domain shift problem where the training and test exam- ples 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 di- rectly suitable for such a comparison, since some of the fea- tures may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Domain Invariant Projection approach: An unsupervised domain adaptation method that overcomes this issue by extracting the information that is invariant across the source and tar- get domains. More specifically, we learn a projection of the data to a low-dimensional latent space where the distance between the empirical distributions of the source and target examples is minimized. We demonstrate the effectiveness of our approach on the task of visual object recognition and show that it outperforms state-of-the-art methods on a stan- dard domain adaptation benchmark dataset

Impact and interest:

54 citations in Scopus
22 citations in Web of Science®
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ID Code: 90968
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Domain Adaptation, Object Recognition, Maximum Mean Discrepancy
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Contact the author
Deposited On: 03 Dec 2015 03:44
Last Modified: 04 Dec 2015 03:59

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