Results 1 to 10 of about 124,232 (320)

A generative adversarial network with multiscale and attention mechanisms for underwater image enhancement [PDF]

open access: yesScientific Reports
Underwater images collected are often of low clarity and suffer from severe color distortion due to the marine environment and Illumination conditions.
Liquan Zhao, Yuda Li, Tie Zhong
doaj   +2 more sources

A Tunnel Crack Segmentation and Recognition Algorithm Using SPGD-and-Generative Adversarial Network Fusion [PDF]

open access: yesSensors
In order to improve the recognition ability of tunnel cracks in the UAV platform with a vision imaging system in the UAV platform with a vision imaging system, this paper proposes a tunnel crack segmentation algorithm using SPGD-and-generative ...
Wei Sun, Xiaohu Liu, Zhiyong Lei
doaj   +2 more sources

Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustment [PDF]

open access: yesScientific Reports
E-commerce platforms have amassed extensive transaction data, which serves as a valuable source for price prediction. However, the diversity of commodities poses challenges such as data imbalance, model overfitting, and underfitting.
Abuduaini Abudureheman   +2 more
doaj   +2 more sources

Super-resolution Reconstruction of MRI Based on DNGAN [PDF]

open access: yesJisuanji kexue, 2022
The quality of MRI will affect doctor's judgment on patient's physical conditions,and the high-resolution MRI is more conducive to doctor to make an accurate diagnosis.Using computer technology to perform super-resolution reconstruction of MRI can obtain
DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang
doaj   +1 more source

ARGAN: Adversarially Robust Generative Adversarial Networks for Deep Neural Networks Against Adversarial Examples

open access: yesIEEE Access, 2022
An adversarial example, which is an input instance with small, intentional feature perturbations to machine learning models, represents a concrete problem in Artificial intelligence safety.
Seok-Hwan Choi   +3 more
doaj   +1 more source

Seismic random noise suppression using improved CycleGAN

open access: yesFrontiers in Earth Science, 2023
Random noise adversely affects the signal-to-noise ratio of complex seismic signals in complex surface conditions and media. The primary challenges related to processing seismic data have always been reducing the random noise and increasing the signal-to-
Shimin Sun   +8 more
doaj   +1 more source

Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN

open access: yesComplexity, 2021
Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time ...
Jianghua Nie   +3 more
doaj   +1 more source

Mechanical and electrical equipment fault diagnosis based on dual attention mechanism and S-BiGAN

open access: yesHangkong gongcheng jinzhan, 2023
The accurate fault diagnosis of mechanical and electrical equipment under the condition of limited label samples is of great significance for improving the health management ability of complex mechanical and electrical equipment.
JIAO Xiaoxuan   +4 more
doaj   +1 more source

BoostNet: A Boosted Convolutional Neural Network for Image Blind Denoising

open access: yesIEEE Access, 2021
Deep convolutional neural networks and generative adversarial networks currently attracted the attention of researchers because it is more effective than conventional representation-based methods.
Duc My Vo   +3 more
doaj   +1 more source

DuCaGAN: Unified Dual Capsule Generative Adversarial Network for Unsupervised Image-to-Image Translation

open access: yesIEEE Access, 2020
With the appearance of Generative Adversarial Network (GAN), image-to-image translation based on a new unified framework has attracted growing interests.
Guifang Shao   +4 more
doaj   +1 more source

Home - About - Disclaimer - Privacy