Comparison on Generative Adversarial Networks - A Study

被引：0

作者：

Sharma, Akanksha ^{[1
]}

Jindal, Neeru ^{[1
]}

Thakur, Abhishek ^{[1
]}

机构：

[1] Thapar Inst Engn & Technol, Dept Elect & Commun, Patiala, Punjab, India

来源：

2018 FIRST INTERNATIONAL CONFERENCE ON SECURE CYBER COMPUTING AND COMMUNICATIONS (ICSCCC 2018) | 2018年

关键词：

Machine learning; Generative Adversarial Networks;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Various new deep learning models have been invented, among which generative adversarial networks have gained exceptional prominence in last four years due to its property of image synthesis. GANs have been utilized in diverse fields ranging from conventional areas like image processing, biomedical signal processing, remote sensing, video generation to even off beat areas like sound and music generation. In this paper, we provide an overview of GANs along with its comparison with other networks, as well as different versions of Generative Adversarial Networks.

引用

页码：391 / 396

页数：6