Generative Adversarial Networks, M.Sc. Thesis Sharif University of Technology ; Haji Mirsadeghi, Mir Omid (Supervisor)
Abstract
In this thesis we try to understand one of the most important subfield of deep learning, the generative adversarial networks. In this framework the goal is to reach a generator that generates samples from a target distribution. The target distribution is usually su- per high dimensional and we only have sample access to it. primarily , this distribution was used to be for set of Images (e.g. images of celebrity faces) and GANs performed well in this setting. In this framework two models work simultaneously: a generator tries to generate realistic samples from the target distribution and a discriminator or critic tries to distinguish real samples from generated (fake) samples or more...
Cataloging briefGenerative Adversarial Networks, M.Sc. Thesis Sharif University of Technology ; Haji Mirsadeghi, Mir Omid (Supervisor)
Abstract
In this thesis we try to understand one of the most important subfield of deep learning, the generative adversarial networks. In this framework the goal is to reach a generator that generates samples from a target distribution. The target distribution is usually su- per high dimensional and we only have sample access to it. primarily , this distribution was used to be for set of Images (e.g. images of celebrity faces) and GANs performed well in this setting. In this framework two models work simultaneously: a generator tries to generate realistic samples from the target distribution and a discriminator or critic tries to distinguish real samples from generated (fake) samples or more...
Find in contentBookmark
|
|