Generative Adversarial Networks in Image Generation and Recognition

被引：2

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

页码：1294 / 1297

页数：4

共 50 条

[21] Data Augmentation with Generative Adversarial Networks for Grocery Product Image Recognition
Wei, Yuchen
Xu, Shuxiang
Son Tran
Kang, Byeong
16TH IEEE INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2020), 2020, : 963 - 968
[22] Popular image generation based on popularity measures by generative adversarial networks
Nezhad, Narges Mohammadi
Mirtaheri, Seyedeh Leili
Shahbazian, Reza
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (14) : 20873 - 20897
[23] Shale Digital Core Image Generation Based on Generative Adversarial Networks
Zha, Wenshu
Li, Xingbao
Li, Daolun
Xing, Yan
He, Lei
Tan, Jieqing
JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME, 2021, 143 (03):
[24] Scalable image generation and super resolution using generative adversarial networks
Turhan, Ceren Guzel
Bilge, Hasan Sakir
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, 2020, 35 (02): : 953 - 966
[25] Wasserstein Generative Adversarial Networks for Realistic Traffic Sign Image Generation
Dewi, Christine
Chen, Rung-Ching
Liu, Yan-Ting
INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2021, 2021, 12672 : 479 - 493
[26] Popular image generation based on popularity measures by generative adversarial networks
Narges Mohammadi Nezhad
Seyedeh Leili Mirtaheri
Reza Shahbazian
Multimedia Tools and Applications, 2023, 82 : 20873 - 20897
[27] Context-aware Image Generation by using Generative Adversarial Networks
Nakamura, Kenki
Ma, Qiang
2017 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 2017, : 516 - 523
[28] Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks
Li, Linyan
Sun, Yu
Hu, Fuyuan
Zhou, Tao
Xi, Xuefeng
Ren, Jinchang
DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2020, 2020
[29] Unregularized Auto-Encoder with Generative Adversarial Networks for Image Generation
Wang, Jiayu
Zhou, Wengang
Tang, Jinhui
Fu, Zhongqian
Tian, Qi
Li, Houqiang
PROCEEDINGS OF THE 2018 ACM MULTIMEDIA CONFERENCE (MM'18), 2018, : 709 - 717
[30] MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computers
Silver, Daniel
Patel, Tirthak
Cutler, William
Ranjan, Aditya
Gandhi, Harshitta
Tiwari, Devesh
2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV, 2023, : 7007 - 7016

← 1 2 3 4 5 →