Generative Adversarial Networks (GANs) have been extremely successful in various application domains. Adversarial image synthesis has drawn increasing attention and made tremendous progress in recent years because of its wide range of applications in many computer vision and image processing problems. Among the many applications of GAN, image synthesis is the most well-studied one, and research in this area has already demonstrated the great potential of using GAN in image synthesis. In this paper, we provide a taxonomy of methods used in image synthesis, review different models for text-to-image synthesis and image-to-image translation, and discuss some evaluation metrics as well as possible future research directions in image synthesis with GAN.

翻译：各种应用领域都取得了极大的成功,反向图像合成近年来引起越来越多的关注,并取得了巨大进展,因为其在许多计算机视觉和图像处理问题中的应用范围很广,在GAN的许多应用中,图像合成是最受研究最深的,这一领域的研究已经表明在图像合成中使用GAN的巨大潜力。在本文件中,我们提供了图像合成方法分类,审查了文本到图像合成和图像到图像翻译的不同模型,并讨论了一些评价指标以及今后可能与GAN图像合成的研究方向。