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

页码：1294 / 1297

页数：4

共 50 条

[31] 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
[32] Context-aware Image Generation by using Generative Adversarial Networks
Nakamura, Kenki
Ma, Qiang
2017 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 2017, : 516 - 523
[33] 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
[34] 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
[35] MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation
Park, David Keetae
Yoo, Seungjoo
Bahng, Hyojin
Choo, Jaegul
Park, Noseong
PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2018, : 878 - 884
[36] Generative Adversarial Networks in Healthcare: A Case Study on MRI Image Generation
Cepa, Beatriz
Brito, Claudia
Sousa, Antonio
2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG, 2023, : 48 - 51
[37] Using Reinforcement Learning to Train Generative Adversarial Networks for Image Generation
Nguyen, Austin
Jin, Rong
2023 IEEE 13TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE, CCWC, 2023, : 160 - 167
[38] Synthetic Traffic Sign Image Generation Applying Generative Adversarial Networks
Dewi, Christine
Chen, Rung-Ching
Liu, Yan-Ting
VIETNAM JOURNAL OF COMPUTER SCIENCE, 2022, 09 (03) : 333 - 348
[39] A THz Passive Image Generation Method Based on Generative Adversarial Networks
Yang, Guan
Li, Chao
Liu, Xiaojun
Fang, Guangyou
APPLIED SCIENCES-BASEL, 2022, 12 (04):
[40] Tile Art Image Generation using Conditional Generative Adversarial Networks
Matsumura, Naoki
Tokura, Hiroki
Kuroda, Yuki
Ito, Yasuaki
Nakano, Koji
2018 SIXTH INTERNATIONAL SYMPOSIUM ON COMPUTING AND NETWORKING WORKSHOPS (CANDARW 2018), 2018, : 209 - 215

← 1 2 3 4 5 →