Document Type

Conference Proceeding

Publication Date

1-1-2019

Publication Title

CCSEA

Publisher

IEEE

Volume

2019

First page number:

1

Last page number:

14

Abstract

In the past few years, Generative Adversarial Networks (GANs) have received immense attention by researchers in a variety of application domains. This new field of deep learning has been growing rapidly and has provided a way to learn deep representations without extensive use of annotated training data. Their achievements may be used in a variety of applications, including speech synthesis, image and video generation, semantic image editing, and style transfer. Image synthesis is an important component of expert systems and it attracted much attention since the introduction of GANs. However, GANs are known to be difficult to train especially when they try to generate high resolution images. This paper gives a thorough overview of the state-of-the-art GANs-based approaches in four applicable areas of image generation including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Aging, and 3D Image Synthesis. Experimental results show state-of-the-art performance using GANs compared to traditional approaches in the fields of image processing and machine vision.

Keywords

Conditional Generative Adversarial Networks (CGANs); Image Synthesis; Image-To-Image Translation; Text-To-Image Synthesis; 3D GANs

Disciplines

Electrical and Computer Engineering | Engineering

Language

English

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