基于GAN网络的目标图像生成方法综述  

Overview of Target Image Generation Methods Based on GAN Networks

作　　者：王培龙 苗壮 王家宝 李阳 李允臣 WANG Peilong;MIAO Zhuang;WANG Jiabao;LI Yang;LI Yunchen(Command and Control Engineering College,Army Engineering University of PLA,Nanjing 210007,China)

机构地区：[1]陆军工程大学指挥控制工程学院,江苏南京210007

出　　处：《软件导刊》2024年第9期10-19,共10页Software Guide

基　　金：江苏省自然科学基金项目(BK20200581)。

摘　　要：生成对抗网络自2014年被提出以来,极大地推动了图像生成研究的进展。其通过两个神经网络的相互博弈,逐步提高鉴别真实图像与生成图像的能力,以及生成逼真图像的能力,最终使双方达到一种纳什均衡。简要介绍生成对抗网络,并围绕生成包含特定对象的图像这一问题对该网络在图像生成领域中的应用方法进行梳理,将其分为直接法、迭代法、分层法、解耦法和3D建模法5种类别。重点关注生成对抗网络在生成包含特定对象的图像方面的研究进展,并对该领域的发展方向进行展望,以期为相关人员进行图像生成研究提供参考。Since its proposal in 2014,generative adversarial networks have greatly promoted the progress of image generation research.Through the mutual game between two neural networks,it gradually improves the ability to distinguish between real images and generate imag-es,as well as the ability to generate realistic images,ultimately achieving a Nash equilibrium between the two parties.Briefly introduce the generation of adversarial networks and sort out their application methods in the field of image generation around the issue of generating images containing targets.They are divided into five categories:direct method,iterative method,hierarchical method,decoupling method,and 3D modeling method.Focus on the research progress of generative adversarial networks in generating images containing targets,and prospect the development direction of image generation methods containing objects,in order to provide reference for relevant researchers in image genera-tion research.

关 键 词：图像生成 生成对抗性网络 目标图像 解耦 人工智能 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]