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.
@article{arxiv.2106.16056,
title = {A Survey on Adversarial Image Synthesis},
author = {William Roy and Glen Kelly and Robert Leer and Frederick Ricardo},
journal= {arXiv preprint arXiv:2106.16056},
year = {2021}
}
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arXiv admin note: submission has been withdrawn by arXiv administrators due to inappropriate text overlap with external source