A Survey on Adversarial Image Synthesis
This is a survey paper that synthesizes existing knowledge for researchers in computer vision and image processing, making it incremental in nature.
The paper provides a taxonomy and review of methods for adversarial image synthesis using GANs, covering text-to-image synthesis and image-to-image translation, and discusses evaluation metrics and future research directions.
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.