  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.
