Analysis of Generative Adversarial Networks (GANs) and Their Variants Based on Encoders and Decoders

Jiteng Fan

2024

Abstract

Generative Adversarial Networks (GANs), are among the most noteworthy advances in machine learning. GANs successfully utilize game concepts to train neural networks. With the deepening of research, a large number of GAN variants have been proposed, which greatly improve the performance of GAN in various aspects. To further analyse GAN, this paper provides a detailed overview. The core objective of this paper is to study the basic ideas of GAN and to explore the principles and performance of some GAN variants in depth. Additionally, the paper evaluates the strengths and weaknesses of each model as well as possible future directions. Based on MNIST and Cifar-10 datasets, this paper analyses the GAN, Conditional GAN (CGAN), Deep Convolutional GGAN (DCGAN) and Big GAN (BigGAN) models using quantitative and qualitative methods. Among them, Inception score (IS), a widely used metric to assess the quality of GAN model generation, was used to compare model performance quantitatively. Based on the experimental results, this study critically compares each GAN variant. In addition, this study discusses the existing limitations of GAN and future research directions.

Paper Citation

in Harvard Style

Fan J. (2024). Analysis of Generative Adversarial Networks (GANs) and Their Variants Based on Encoders and Decoders. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 343-348. DOI: 10.5220/0012937900004508

in Bibtex Style

@conference{emiti24,
author={Jiteng Fan},
title={Analysis of Generative Adversarial Networks (GANs) and Their Variants Based on Encoders and Decoders},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={343-348},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012937900004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Analysis of Generative Adversarial Networks (GANs) and Their Variants Based on Encoders and Decoders
SN - 978-989-758-713-9
AU - Fan J.
PY - 2024
SP - 343
EP - 348
DO - 10.5220/0012937900004508
PB - SciTePress