Volltext-Downloads (blau) und Frontdoor-Views (grau)
The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 37 of 871
Back to Result List

Generative adversarial networks: project relevant overview

  • Generating synthetic data is a relevant point in the machine learning community. As accessible data is limited, the generation of synthetic data is a significant point in protecting patients' privacy and having more possibilities to train a model for classification or other machine learning tasks. In this work, some generative adversarial networks (GAN) variants are discussed, and an overview is given of how generative adversarial networks can be used for data generation in different fields. In addition, some common problems of the GANs and possibilities to avoid them are shown. Different evaluation methods of the generated data are also described.

Download full text files

Export metadata

Additional Services

Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Author of HS ReutlingenMartínez Madrid, Natividad; Kraft, Rodion
URN:urn:nbn:de:bsz:rt2-opus4-40027
DOI:https://doi.org/10.34645/opus-4002
ISBN:978-3-00-074291-0
Erschienen in:Hardware and software supporting physiological measurement (HSPM-2022)
Publisher:Hochschule Reutlingen
Place of publication:Reutlingen
Document Type:Conference proceeding
Language:English
Publication year:2022
Page Number:3
First Page:23
Last Page:25
DDC classes:004 Informatik
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International