TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Kraft, Rodion
A1  - Martínez Madrid, Natividad
A1  - Seepold, Ralf
T1  - Generative adversarial networks: project relevant overview
T2  - Hardware and software supporting physiological measurement (HSPM-2022)
N2  - 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.
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-40027
SN  - 978-3-00-074291-0
SB  - 978-3-00-074291-0
U6  - https://doi.org/10.34645/opus-4002
DO  - https://doi.org/10.34645/opus-4002
SP  - 23
EP  - 25
S1  - 3
PB  - Hochschule Reutlingen
CY  - Reutlingen
ER  -