Search Results for author: Yimin Yang

Found 11 papers, 2 papers with code

Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

no code implementations19 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.

Generative Adversarial Network Image Super-Resolution +1

Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network

no code implementations18 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).

Image Super-Resolution

Analytic Learning of Convolutional Neural Network For Pattern Recognition

no code implementations14 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.

AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks

1 code implementation10 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.

Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection

no code implementations20 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.

Anomaly Detection

Deconvolution-and-convolution Networks

no code implementations22 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.

Multi-Model Least Squares-Based Recomputation Framework for Large Data Analysis

no code implementations4 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.

Representation Learning

Deep Networks with Fast Retraining

no code implementations13 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.

Image Classification

Non-iterative recomputation of dense layers for performance improvement of DCNN

no code implementations14 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).

Object Recognition

Pulling back error to the hidden-node parameter technology: Single-hidden-layer feedforward network without output weight

no code implementations6 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.