A visual domain adaptation method based on enhanced subspace distribution matching

Document Type

Conference Proceeding

Publication Date

5-18-2018

Abstract

One of the challenges in computer vision is how to learn an accurate classifier for a new domain by using labeled images from an old domain under the condition that there is no available labeled images in the new domain. Domain adaptation is an outstanding solution that tackles this challenge by employing available source-labeled datasets, even with significant difference in distribution and properties. However, most prior methods only reduce the difference in subspace marginal or conditional distributions across domains while completely ignoring the source data label dependence information in a subspace. In this paper, we put forward a novel domain adaptation approach, referred to as Enhanced Subspace Distribution Matching. Specifically, it aims to jointly match the marginal and conditional distributions in a kernel principal dimensionality reduction procedure while maximizing the source label dependence in a subspace, thus raising the subspace distribution matching degree. Extensive experiments verify that it can significantly outperform several state-of-the-art methods for cross-domain image classification problems.

Identifier

85048248953 (Scopus)

ISBN

[9781538650530]

Publication Title

Icnsc 2018 15th IEEE International Conference on Networking Sensing and Control

External Full Text Location

https://doi.org/10.1109/ICNSC.2018.8361269

First Page

1

Last Page

6

Grant

51775385

Fund Ref

National Natural Science Foundation of China

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