1 code implementation • 15 Apr 2022 • Kuangen Zhang, Jiahong Chen, Jing Wang, Xinxing Chen, Yuquan Leng, Clarence W. de Silva, Chenglong Fu
EDH mitigates the divergence between labeled data of source subjects and unlabeled data of target subjects to accurately classify the locomotion modes of target subjects without labeling data.
no code implementations • 9 Jan 2022 • Jiahong Chen, Jing Wang, Weipeng Lin, Kuangen Zhang, Clarence W. de Silva
Recent advances in unsupervised domain adaptation have shown that mitigating the domain divergence by extracting the domain-invariant representation could significantly improve the generalization of a model to an unlabeled data domain.
no code implementations • 25 Sep 2021 • Eric McCormick, Haoxiang Lang, Clarence W. de Silva
This paper proposes a methodology of integrating the Linear Graph (LG) approach with Genetic Programming (GP) for generating an automated multi-domain engineering design approach by using the in-house developed LG MATLAB toolbox and the GP toolbox in MATLAB.
no code implementations • 29 Aug 2020 • Kuangen Zhang, Jongwoo Lee, Zhimin Hou, Clarence W. de Silva, Chenglong Fu, Neville Hogan
This paper focuses on the latter because the structured policy is more intuitive and can inherit insights from previous model-based controllers.
1 code implementation • 23 Jun 2020 • Jing Wang, Jiahong Chen, Jianzhe Lin, Leonid Sigal, Clarence W. de Silva
To solve this problem, we introduce a Gaussian-guided latent alignment approach to align the latent feature distributions of the two domains under the guidance of the prior distribution.
Ranked #1 on Domain Adaptation on SYNSIG-to-GTSRB
no code implementations • 5 Jun 2020 • Jun Ma, Haiyue Zhu, Xiaocong Li, Wenxin Wang, Clarence W. de Silva, Tong Heng Lee
It is also noteworthy that the proposed methodology additionally provides the necessary and sufficient conditions for this robust controller design with the consideration of a prescribed finite frequency range, and therefore significantly less conservatism is attained in the system performance.
1 code implementation • 22 Oct 2019 • Kuangen Zhang, Zhimin Hou, Clarence W. de Silva, Haoyong Yu, Chenglong Fu
However, the local minima caused by unsuitable rewards and the overestimation of the cumulative reward impede the maximization of the cumulative reward.
1 code implementation • 22 Apr 2019 • Kuangen Zhang, Ming Hao, Jing Wang, Clarence W. de Silva, Chenglong Fu
Learning on point cloud is eagerly in demand because the point cloud is a common type of geometric data and can aid robots to understand environments robustly.
1 code implementation • 22 Oct 2017 • Lili Meng, Jianhui Chen, Frederick Tung, James J. Little, Julien Valentin, Clarence W. de Silva
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection.