Conclusion
Document Type
Article
Source of Publication
SpringerBriefs in Computer Science
Publication Date
1-1-2023
Abstract
GANs are a type of deep learning model subset of machine learning that has been a hot area of research in recent years. Its architecture comprises two networks: the generator and the discriminator are mainly made up of a deep neural network. The generator model aims to generate new data that looks like the real one. Typically, the generator model is composed of multiple up-sampling layers. The discriminator aimed to differentiate between actual data and generated one. The discriminative model is made of multiple down-sapling layers. The discriminator is a typical binary classifier deep neural network. The discriminator gives the generator feedback to update its weights and yield more realistic results.
DOI Link
ISSN
Publisher
Springer International Publishing
Volume
Part F822
First Page
83
Last Page
84
Disciplines
Computer Sciences
Keywords
Availability of datasets, Challenges, Hyperparameter tuning, Training dataset
Scopus ID
Recommended Citation
Kaddoura, Sanaa, "Conclusion" (2023). All Works. 5914.
https://zuscholars.zu.ac.ae/works/5914
Indexed in Scopus
yes
Open Access
no