Thesis Details
Generating Faces with Generative Adversarial Networks
The goal of this thesis is generating color images of faces from randomly chosen high-dimensional vectors with Generative Adversarial Networks. The next task is to analyze input vectors based on the features of faces generated from those vectors. Three different models of Generative Adversarial Network are implemented, one for generating images of handwritten digits and other two for generating images of faces. Generated images show credible-looking faces, but recognizable from real ones with a human eye. Single dimensions of input vectors are analyzed with Student's t-test. Linear Discriminant Analysis is then used to project input vectors into subspaces where the classes of features are separable. Analysis of generated data proves that the input vector can be specifically chosen to generate an image of a face with requested features with probability up to 80 %. The main result of this thesis is a model of Generative Adversarial Network for generating images of faces. A tool for generating images of faces with chosen features is implemented too.
machine learning, deep learning, neural network, generative model, generating, generative adversarial network, GAN, image, face
Bidlo Michal, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Čadík Martin, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Grégr Matěj, Ing., Ph.D. (DIFS FIT BUT), člen
Kočí Radek, Ing., Ph.D. (DITS FIT BUT), člen
@bachelorsthesis{FITBT22319,
author = "Daniel Kone\v{c}n\'{y}",
type = "Bachelor's thesis",
title = "Generating Faces with Generative Adversarial Networks",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2020,
location = "Brno, CZ",
language = "english",
url = "https://www.fit.vut.cz/study/thesis/22319/"
}