Home > Published Issues > 2022 > Volume 13, No. 1, February 2022 >
JAIT 2022 Vol.13(1): 21-28
doi: 10.12720/jait.13.1.21-28

Balanced Weight Joint Geometrical and Statistical Alignment for Unsupervised Domain Adaptation

M. S. Rizal Samsudin, Syed A. R. Abu-Bakar, and Musa M. Mokji
School of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia

Abstract—In real-world applications, images taken from different cameras usually have different resolution, illumination, poses, and background views. This problem leads to the need of domain adaptation in which case, training and testing are not drawn from the same distribution. There have been many studies carried out on domain adaptation, and among the state-of-the-art methods is the Joint Geometrical and Statistical Alignment (JGSA) approach. This paper presents an improvement for unsupervised domain adaptation in transfer learning using a Balanced Weight JGSA (BW-JGSA). The existing method of JGSA seeking the way to minimize the distribution divergence between marginal and conditional distribution across domains; however, treat them equally in terms of distribution weight. This drawback affects the existing method mainly when applied to real applications. The contribution of this paper is to use balanced distribution adaptation in JGSA that can adaptively leverage the importance of marginal and conditional distribution in JGSA. In this method, the balance weight factor, μ, will be applied to marginal and conditional distributions distance for each different subspace in JGSA. Comparing the proposed method with state-of-the-art techniques in object and digital datasets shows significant improvement of our work.
Index Terms—domain adaptation, transfer learning, the balanced weight, joint geometrical, and statistical alignment

Cite: M. S. Rizal Samsudin, Syed A. R. Abu-Bakar, and Musa M. Mokji, "Balanced Weight Joint Geometrical and Statistical Alignment for Unsupervised Domain Adaptation," Journal of Advances in Information Technology, Vol. 13, No. 1, pp. 21-28, February 2022. 

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.