找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: GANs for Data Augmentation in Healthcare; Arun Solanki,Mohd Naved Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusi

[复制链接]
楼主: incoherent
发表于 2025-3-26 23:49:53 | 显示全部楼层
Ökonomische Implikationen des Bosman-Urteilsersarial networks (GANs) have been employed for data augmentation for refining the deep learning models by generating additional information with no pre-planned process to generate realistic samples from the existing data and improve the model performance. Wasserstein Generative Adversarial Network
发表于 2025-3-27 01:53:39 | 显示全部楼层
发表于 2025-3-27 06:33:27 | 显示全部楼层
发表于 2025-3-27 09:36:35 | 显示全部楼层
Chest X-Ray Data Augmentation with Generative Adversarial Networks for Pneumonia and COVID-19 Diagnplement chest X-rays. We demonstrate that our GAN-based techniques for data augmentation outperforms previous traditional data augmentation techniques to train a GAN in identifying abnormalities in chest X-ray images by comparing our data augmentation GAN method with DCGAN (Deep Convolutional Genera
发表于 2025-3-27 16:44:47 | 显示全部楼层
State of the Art Framework-Based Detection of GAN-Generated Face Images, The inception-based model topped the list with a test accuracy of 99%. The ResNet and EfficientNet models were tied for second place with 97% testing accuracy. A separate five-fold-cross-validation method was also performed in comparison to the holdout method. Though this is a specific use case, we
发表于 2025-3-27 20:03:28 | 显示全部楼层
发表于 2025-3-27 22:54:24 | 显示全部楼层
Geometric Transformations-Based Medical Image Augmentation,ion-based data augmentation segments the infected area and the classification process is proposed to highlight the severity of the disease. The proposed suggests an impartial and all-encompassing framework of evaluation for various information augmentation techniques. With this cutting-edge procedur
发表于 2025-3-28 03:36:17 | 显示全部楼层
发表于 2025-3-28 06:33:27 | 显示全部楼层
发表于 2025-3-28 13:53:23 | 显示全部楼层
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-26 00:18
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表