Results 41 to 50 of about 219,883 (315)

Towards Unsupervised Deep Image Enhancement With Generative Adversarial Network [PDF]

IEEE Transactions on Image Processing, 2020
Improving the aesthetic quality of images is challenging and eager for the public. To address this problem, most existing algorithms are based on supervised learning methods to learn an automatic photo enhancer for paired data, which consists of low ...
Zhangkai Ni, Wenhan Yang, Shiqi Wang, Lin Ma, S. Kwong   +4 more
semanticscholar   +1 more source

Survey of generative adversarial network

网络与信息安全学报, 2021
Firstly, the basic theory, application scenarios and current state of research of GAN (generative adversarial network) were introduced, and the problems need to be improved were listed. Then, recent research, improvement mechanism and model features in 2
WANG Zhenglong, ZHANG Baowen
doaj   +3 more sources

Text Generation Based on Generative Adversarial Nets with Latent Variable

, 2018
In this paper, we propose a model using generative adversarial net (GAN) to generate realistic text. Instead of using standard GAN, we combine variational autoencoder (VAE) with generative adversarial net. The use of high-level latent random variables is
Qin, Zengchang, Wan, Tao, Wang, Heng
core   +1 more source

Lung image segmentation via generative adversarial networks

Frontiers in Physiology
IntroductionLung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment.MethodsThis paper explores the lung CT image segmentation method by generative adversarial networks.
Jiaxin Cai, Hongfeng Zhu, Siyu Liu, Yang Qi, Rongshang Chen   +4 more
doaj   +1 more source

Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl

Applied Sciences, 2023
This paper introduces a novel and robust data-driven algorithm designed for Aircraft Trajectory Prediction (ATP). The approach employs a Neural Network architecture to predict future aircraft trajectories, utilizing input variables such as latitude ...
Seyed Mohammad Hashemi, Seyed Ali Hashemi, Ruxandra Mihaela Botez, Georges Ghazi   +3 more
doaj   +1 more source

GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction [PDF]

Computer Vision and Pattern Recognition, 2019
In the past few years, a lot of work has been done towards reconstructing the 3D facial structure from single images by capitalizing on the power of Deep Convolutional Neural Networks (DCNNs).
Baris Gecer, Stylianos Ploumpis, I. Kotsia, S. Zafeiriou   +3 more
semanticscholar   +1 more source

Attentively Conditioned Generative Adversarial Network for Semantic Segmentation

IEEE Access, 2020
Generative Adversarial Network has proven to produce state-of-the-art results by framing a generative modeling task into a supervised learning problem. In this paper, we propose Attentively Conditioned Generative Adversarial Network (ACGAN) for semantic ...
Ariyo Oluwasanmi, Muhammad Umar Aftab, Akeem Shokanbi, Jehoiada Jackson, Bulbula Kumeda, Zhiquang Qin   +5 more
doaj   +1 more source

PAMSGAN: Pyramid Attention Mechanism-Oriented Symmetry Generative Adversarial Network for Motion Image Deblurring

IEEE Access, 2021
Motion blur is a common problem in optical imaging, which is caused by the relative displacement between the subject and the camera in the exposure process of the camera.
Zhenfeng Zhang
doaj   +1 more source

Adversarial Spatio-Temporal Learning for Video Deblurring

, 2018
Camera shake or target movement often leads to undesired blur effects in videos captured by a hand-held camera. Despite significant efforts having been devoted to video-deblur research, two major challenges remain: 1) how to model the spatio-temporal ...
Li, Hongdong, Liu, Wei, Luo, Wenhan, Ma, Lin, Zhang, Kaihao, Zhong, Yiran   +5 more
core   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source