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Performance comparison between generative adversarial networks (GAN) variants in generating comic character images.

Generative Adversarial Networks (GANs) have emerged as a powerful framework for generating realistic and diverse data, including images. This project aims to provide a comprehensive understanding of GANs and their applications in anime face generation. Through theoretical investigation, practical im...

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Main Author: Tan, Jia Ler
Format: Final Year Project / Dissertation / Thesis
Published: 2024
Subjects:
T Technology (General)
Online Access:http://eprints.utar.edu.my/6674/1/fyp_CS_2024_TJL.pdf
http://eprints.utar.edu.my/6674/
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spelling my-utar-eprints.66742024-10-23T06:35:55Z Performance comparison between generative adversarial networks (GAN) variants in generating comic character images. Tan, Jia Ler T Technology (General) Generative Adversarial Networks (GANs) have emerged as a powerful framework for generating realistic and diverse data, including images. This project aims to provide a comprehensive understanding of GANs and their applications in anime face generation. Through theoretical investigation, practical implementation, and empirical analysis, the project explores the working principles of GANs, including their architecture, training dynamics, and variants. The focus is on prominent GAN architectures such as Deep Convolutional GANs (DCGAN), CycleGAN, and Spectral Normalization GAN (SNGAN). The project conducts a thorough performance analysis of these GAN architectures in anime face generation tasks. This involves collecting and preprocessing anime face datasets, training GAN models, and evaluating their performance using quantitative metrics. The quality and diversity of generated anime face images are analyzed using FID and IS score. Furthermore, a comparative analysis of DCGAN, CycleGAN, and SNGAN is conducted to identify their strengths and weaknesses. This comparative study provides insights into the suitability of different GAN architectures for anime face generation applications. The project aims to contribute to the advancement of knowledge in the field of GANs. 2024-01 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6674/1/fyp_CS_2024_TJL.pdf Tan, Jia Ler (2024) Performance comparison between generative adversarial networks (GAN) variants in generating comic character images. Final Year Project, UTAR. http://eprints.utar.edu.my/6674/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic T Technology (General)
spellingShingle T Technology (General)
Tan, Jia Ler
Performance comparison between generative adversarial networks (GAN) variants in generating comic character images.
description Generative Adversarial Networks (GANs) have emerged as a powerful framework for generating realistic and diverse data, including images. This project aims to provide a comprehensive understanding of GANs and their applications in anime face generation. Through theoretical investigation, practical implementation, and empirical analysis, the project explores the working principles of GANs, including their architecture, training dynamics, and variants. The focus is on prominent GAN architectures such as Deep Convolutional GANs (DCGAN), CycleGAN, and Spectral Normalization GAN (SNGAN). The project conducts a thorough performance analysis of these GAN architectures in anime face generation tasks. This involves collecting and preprocessing anime face datasets, training GAN models, and evaluating their performance using quantitative metrics. The quality and diversity of generated anime face images are analyzed using FID and IS score. Furthermore, a comparative analysis of DCGAN, CycleGAN, and SNGAN is conducted to identify their strengths and weaknesses. This comparative study provides insights into the suitability of different GAN architectures for anime face generation applications. The project aims to contribute to the advancement of knowledge in the field of GANs.
format Final Year Project / Dissertation / Thesis
author Tan, Jia Ler
author_facet Tan, Jia Ler
author_sort Tan, Jia Ler
title Performance comparison between generative adversarial networks (GAN) variants in generating comic character images.
title_short Performance comparison between generative adversarial networks (GAN) variants in generating comic character images.
title_full Performance comparison between generative adversarial networks (GAN) variants in generating comic character images.
title_fullStr Performance comparison between generative adversarial networks (GAN) variants in generating comic character images.
title_full_unstemmed Performance comparison between generative adversarial networks (GAN) variants in generating comic character images.
title_sort performance comparison between generative adversarial networks (gan) variants in generating comic character images.
publishDate 2024
url http://eprints.utar.edu.my/6674/1/fyp_CS_2024_TJL.pdf
http://eprints.utar.edu.my/6674/
_version_ 1814061983159287808
score 13.251813

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