Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)

<Previous Article In Volume
Next Article In Volume>

Exploring Deep Learning-Based Generative Image Techniques: Methods and Applications

Authors
Yi Huang1, *
1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
*Corresponding author. Email: YiHuang_Essen@njust.edu.cn
Corresponding Author
Yi Huang
Available Online 23 September 2024.
DOI
10.2991/978-94-6463-512-6_52How to use a DOI?
Keywords
Generative models; Generative Adversarial Networks; Autoregressive Models
Abstract

The pervasive integration of deep learning within the realm of image generation has catalyzed profound advancements and breakthroughs in this technology. The advent of emblematic models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) has positioned image generation at the forefront of discussions in computer vision and artificial intelligence. This paper delves into these three quintessential deep learning-based image generation models: GANs, VAEs, and Autoregressive Models (ARMs). It offers an in-depth examination of their methodologies, recent enhancements, and the trajectories of their development, aiming to elucidate the current landscape of image generation technologies and the practical challenges they encounter. Furthermore, the paper projects future trends and potential avenues for research in image generation, spotlighting emergent areas of scholarly interest. By presenting a comprehensive review of extant image generation technologies, this manuscript seeks to furnish invaluable insights and resources for researchers in allied domains, thereby fostering the further evolution and utilization of image generation technology.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
Series
Advances in Intelligent Systems Research
Publication Date
23 September 2024
ISBN
978-94-6463-512-6
ISSN
1951-6851
DOI
10.2991/978-94-6463-512-6_52How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yi Huang
PY  - 2024
DA  - 2024/09/23
TI  - Exploring Deep Learning-Based Generative Image Techniques: Methods and Applications
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 488
EP  - 501
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_52
DO  - 10.2991/978-94-6463-512-6_52
ID  - Huang2024
ER  -