no code implementations • 19 Nov 2022 • Xiang Wang, Yimin Yang, Zhichang Guo, Zhili Zhou, Yu Liu, Qixiang Pang, Shan Du
First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain.
no code implementations • 18 Nov 2022 • Xiang Wang, Yimin Yang, Qixiang Pang, Xiao Lu, Yu Liu, Shan Du
In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e. g., 4x and 8x).
1 code implementation • CVPR 2022 • Xiao Lu, Yihong Cao, Sheng Liu, Chengjiang Long, Zipei Chen, Xuanyu Zhou, Yimin Yang, Chunxia Xiao
Our proposed approach is extensively validated on the ViSha dataset and a self-annotated dataset.
no code implementations • 14 Feb 2022 • Huiping Zhuang, Zhiping Lin, Yimin Yang, Kar-Ann Toh
Training convolutional neural networks (CNNs) with back-propagation (BP) is time-consuming and resource-intensive particularly in view of the need to visit the dataset multiple times.
1 code implementation • 10 Feb 2022 • Yimin Yang, Siamak Mehrkanoon
Data driven modeling based approaches have recently gained a lot of attention in many challenging meteorological applications including weather element forecasting.
no code implementations • 20 Dec 2021 • Yurong Chen, HUI ZHANG, Yaonan Wang, Q. M. Jonathan Wu, Yimin Yang
In this case, the Wasserstein distance can be calculated with the closed-form, even the prior distribution is not Gaussian.
no code implementations • 22 Mar 2021 • Yimin Yang, Wandong Zhang, Jonathan Wu, Will Zhao, Ao Chen
2D Convolutional neural network (CNN) has arguably become the de facto standard for computer vision tasks.
no code implementations • 4 Jan 2021 • Wandong Zhang, QM Jonathan Wu, Yimin Yang, WG Will Zhao, Tianlei Wang, HUI ZHANG
Most multilayer least squares (LS)-based neural networks are structured with two separate stages: unsupervised feature encoding and supervised pattern classification.
no code implementations • 13 Aug 2020 • Wandong Zhang, Yimin Yang, Jonathan Wu
Compared to other learning strategies, the proposed learning pipeline has robustness against the hyper-parameters, and the requirement of computational resources is significantly reduced.
no code implementations • 14 Sep 2018 • Yimin Yang, Q. M. Jonathan Wu, Xiexing Feng, Thangarajah Akilan
An iterative method of learning has become a paradigm for training deep convolutional neural networks (DCNN).
no code implementations • 6 May 2014 • Yimin Yang, Q. M. Jonathan Wu, Guang-Bin Huang, Yaonan Wang
SLFNs are universal approximators when at least the parameters of the networks including hidden-node parameter and output weight are exist.