Subspace Distribution Alignment for Unsupervised Domain Adaptation

Baochen Sun and Kate Saenko

Abstract

We propose a novel method for unsupervised domain adaptation. Traditional machine learning algorithms often fail to generalize to new input distributions, causing reduced accuracy. Domain adaptation attempts to compensate for the performance degradation by transferring and adapting source knowledge to target domain. Existing unsupervised methods project domains into a lower-dimensional space and attempt to align the subspace bases, effectively learning a mapping from source to target points or vice versa. However, they fail to take into account the difference of the two distributions in the subspaces, resulting in misalignment even after adaptation. We present a unified view of existing subspace mapping based methods and develop a generalized approach that also aligns the distributions as well as the subspace bases. We provide a detailed evaluation of our approach on benchmark datasets and show improved results over published approaches.

Session

Poster 1

Files

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PDF iconPaper (PDF, 1994K)

DOI

10.5244/C.29.24
https://dx.doi.org/10.5244/C.29.24

Citation

Baochen Sun and Kate Saenko. Subspace Distribution Alignment for Unsupervised Domain Adaptation. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 24.1-24.10. BMVA Press, September 2015.

Bibtex

@inproceedings{BMVC2015_24,
	title={Subspace Distribution Alignment for Unsupervised Domain Adaptation},
	author={Baochen Sun and Kate Saenko},
	year={2015},
	month={September},
	pages={24.1-24.10},
	articleno={24},
	numpages={10},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.24},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.24}
}