  There has been a drastic growth of research in Generative Adversarial Nets
(GANs) in the past few years. Proposed in 2014, GAN has been applied to various
applications such as computer vision and natural language processing, and
achieves impressive performance. 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.
