no code implementations • 24 Nov 2023 • Yuan Qing, Naixing Wu, Shaohua Wan, Lixin Duan
In the source domain, some training samples are of low-relevance to target domain due to the difference in viewpoints, action styles, etc.
no code implementations • 26 Jul 2022 • Guangchen Shi, Yirui Wu, Jun Liu, Shaohua Wan, Wenhai Wang, Tong Lu
Second, to resist overfitting issues caused by few training samples, a hyper-class embedding is learned by clustering all category embeddings for initialization and aligned with category embedding of the new class for enhancement, where learned knowledge assists to learn new knowledge, thus alleviating performance dependence on training data scale.
no code implementations • 4 Apr 2022 • Meysam Ghahramani, Rahim Taheri, Mohammad Shojafar, Reza Javidan, Shaohua Wan
In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment.
no code implementations • 22 Feb 2021 • Sotirios K. Goudos, Achilles D. Boursianis, Ali Wagdy Mohamed, Shaohua Wan, Panagiotis Sarigiannidis, George K. Karagiannidis, Ponnuthurai N. Suganthan
To the best of the authors knowledge, this is the first time that LGSO algorithms are applied to the optimal power allocation problem in IoT networks.
no code implementations • 8 Mar 2020 • Lone Wong, Deli Zhao, Shaohua Wan, Bo Zhang
Progressive growing enhances image resolution gradually, thereby preserving precision of recovered image.
1 code implementation • 19 Mar 2019 • Hailong Ma, Xiangxiang Chu, Shaohua Wan, Bo Zhang
In recent years, deep learning methods have achieved impressive results with higher peak signal-to-noise ratio in single image super-resolution (SISR) tasks by utilizing deeper layers.
no code implementations • 7 Aug 2016 • Shaohua Wan, Zhijun Chen, Tao Zhang, Bo Zhang, Kong-kat Wong
Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks.