@article{Wu2017, 
author = {Xian Wu and Kun Xu and Peter Hall},
title = {A Survey of Image Synthesis and Editing with Generative Adversarial Networks},
year = {2017},
journal = {Tsinghua Science and Technology},
volume = {22},
number = {6},
pages = {660-674},
keywords = {image synthesis, image editing, generative adversarial networks, constrained image synthesis, image-to-image translation},
url = {https://www.sciopen.com/article/10.23919/TST.2017.8195348},
doi = {10.23919/TST.2017.8195348},
abstract = {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 applications. This paper surveys recent GAN papers regarding topics including, but not limited to, texture synthesis, image inpainting, image-to-image translation, and image editing.}
}