Please use this identifier to cite or link to this item:
Title: Realistic face generation using deep neural networks (StyleGAN)
Authors: Toh, Wilson Chin Shen
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Toh, W. C. S. (2021). Realistic face generation using deep neural networks (StyleGAN). Final Year Project (FYP), Nanyang Technological University, Singapore.
Abstract: An investigation into the baseline GAN and progressive GAN (PGGAN) and subsequent works like the style-based GAN architectures (StyleGAN & StyleGAN2) for facial feature disentanglement. Analysis of the structure of the latent space and random distribution will lead to an understanding of the image generation process. In addition, high-level features such as background and foreground, and fine-grained details such as the features of generated images will be discussed. Exploration of various feature disentanglement structures will be done for understanding. Ultimately, a feature disentangling structure based on representation learning architectures will be proposed.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
2.45 MBAdobe PDFView/Open

Page view(s)

Updated on May 15, 2022

Download(s) 50

Updated on May 15, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.