Generative Adversarial Networks in Image Generation and Recognition

被引：0

作者：

Popuri, Anoushka ^{[1
]}

Miller, John ^{[1
]}

机构：

[1] Francis Howell Sch Dist, O Fallon, MO 63368 USA

来源：

2023 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE, CSCI 2023 | 2023年

关键词：

Generative Adversarial Networks (GANs); Image Recognition; Image Generation; Multi-modal analysis; Text-to-Image Generation; Unsupervised learning; Scalability;

D O I：

10.1109/CSCI62032.2023.00212

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Generative Adversarial Network (GAN) is a class of Generative Machine Learning frameworks, which has shown remarkable promise in the field of synthetic data generation. GANs consist of a generative model and a discriminative model working in a game like contest to generate data with high levels of accuracy. This paper delves into the applications of GANs in the field of Image Generation and Recognition. We look into the advantages and challenges of using GANs, and the ongoing areas of research and improvements, and potential breakthroughs.

引用

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页码：1294 / 1297

页数：4

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