A Survey of Image Synthesis and Editing with Generative Adversarial Networks

作     者：Xian Wu Kun Xu Peter Hall 

作者机构：TNList and the Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China the Department of Computer ScienceUniversity of BathBathUK 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2017年第22卷第6期

页      面：660-674页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the National Key Technology R&D Program(No.2016YFB1001402) the National Natural Science Foundation of China(No.61521002) the Joint NSFC-ISF Research Program(No.61561146393) Research Grant of Beijing Higher Institution Engineering Research Center and Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology supported by the EPSRC CDE(No.EP/L016540/1) 

主　　题：image synthesis image editing constrained image synthesis generative adversarial networks imageto image translation 

摘      要：This paper presents a survey of image synthesis and editing with Generative Adversarial Networks（GANs）. GANs consist of two deep networks, a generator and a discriminator, which are trained in a competitive way. Due to the power of deep networks and the competitive training manner, GANs are capable of producing reasonable and realistic images, and have shown great capability in many image synthesis and editing *** paper surveys recent GAN papers regarding topics including, but not limited to, texture synthesis, image inpainting, image-to-image translation, and image editing.