Formation of alternative approaches to noise generation in gan networks

Публікація:
Formation of alternative approaches to noise generation in gan networks

dc.contributor.author	Bilokon, V. A.
dc.date.accessioned	2024-08-26T14:59:32Z
dc.date.available	2024-08-26T14:59:32Z
dc.date.issued	2024
dc.description.abstract	This work discusses the problem of developing alternative approaches to generating noise in generative adversarial networks (GAN). Various noise generation techniques are important for training GAN networks as they help improve the quality of the generated data and the stability of training. This article provides an overview of current noise generation methods and discusses an approach based on the Pandas library in the Python programming language for generating, storing, and mixing noises.
dc.identifier.citation	Bilokon V. A. Formation of alternative approaches to noise generation in gan networks / V. A. Bilokon ; Scientific supervisor Ph.D., prof. N. V. Ryabova // Радіоелектроніка та молодь у XXI столітті : матеріали 28-го Міжнар. молодіж. форуму, 16–18 квітня 2024 р. – Харків : ХНУРЕ, 2024. – Т. 6 – С. 94-96. – DOI : https://doi.org/10.30837/IYF.IIS.2024.094.
dc.identifier.doi	https://doi.org/10.30837/IYF.IIS.2024.094
dc.identifier.uri	https://openarchive.nure.ua/handle/document/28143
dc.language.iso	en
dc.publisher	ХНУРЕ
dc.subject	gan networks
dc.title	Formation of alternative approaches to noise generation in gan networks
dc.type	Thesis
dspace.entity.type	Publication