Overview of GAN Structure
Document Type
Article
Source of Publication
SpringerBriefs in Computer Science
Publication Date
1-1-2023
Abstract
This chapter will introduce generative adversarial networks (GANs), a type of neural network that can generate new data samples that resemble a given dataset. The chapter will discuss the difference between generative and discriminative models, an overview of the basic architecture of GANs, which consists of a generator and a discriminator, and how they are trained using an adversarial process. We also discuss the weaknesses of GANs and highlight some of their current challenges and limitations.
DOI Link
ISSN
Publisher
Springer International Publishing
Volume
Part F822
First Page
1
Last Page
11
Disciplines
Computer Sciences
Keywords
Convolutional neural network, Discriminative model, Fake data, Generative model, Loss function, Real data
Scopus ID
Recommended Citation
Kaddoura, Sanaa, "Overview of GAN Structure" (2023). All Works. 5911.
https://zuscholars.zu.ac.ae/works/5911
Indexed in Scopus
yes
Open Access
no