AIRCC's International Journal of Computer Science and Information Technology

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In recent years, frameworks that employ Generative Adversarial Networks (GANs) have achieved immense results for various applications in many fields especially those related to image generation both due to their ability to create highly realistic and sharp images as well as train on huge data sets. However, successfully training GANs are notoriously difficult task in case ifhigh resolution images are required. In this article, we discuss five applicable and fascinating areas for image synthesis based on the state-of-theart GANs techniques including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Manipulation, 3D Image Synthesis and DeepMasterPrints. We provide a detailed review of current GANs-based image generation models with their advantages and disadvantages.The results of the publications in each section show the GANs based algorithmsAREgrowing fast and their constant improvement, whether in the same field or in others, will solve complicated image generation tasks in the future.

Keywords

Conditional Generative Adversarial Networks (cGANs), DeepMasterPrints, Face Manipulation, Text-to- Image Synthesis, 3D GAN.
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