Unsupervised domain adaptation using parallel transport on Grassmann manifold

Ashish Shrivastava, Sumit Shekhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

When designing classifiers for classification tasks, one is often confronted with situations where data distributions in the source domain are different from those present in the target domain. This problem of domain adaptation is an important problem that has received a lot of attention in recent years. In this paper, we study the challenging problem of unsupervised domain adaptation, where no labels are available in the target domain. In contrast to earlier works, which assume a single domain shift between the source and target domains, we allow for multiple domain shifts. Towards this, we develop a novel framework based on the parallel transport of union of the source subspaces on the Grassmann manifold. Various recognition experiments show that this way of modeling data with union of subspaces instead of a single subspace improves the recognition performance.

Original languageEnglish (US)
Title of host publication2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
PublisherIEEE Computer Society
Pages277-284
Number of pages8
ISBN (Print)9781479949854
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States
Duration: Mar 24 2014Mar 26 2014

Publication series

Name2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Other

Other2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
CountryUnited States
CitySteamboat Springs, CO
Period3/24/143/26/14

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Shrivastava, A., & Shekhar, S. (2014). Unsupervised domain adaptation using parallel transport on Grassmann manifold. In 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 (pp. 277-284). [6836088] (2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014). IEEE Computer Society. https://doi.org/10.1109/WACV.2014.6836088