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.
| Author: | Rodion KraftORCiD, Natividad Martínez MadridORCiD, Ralf SeepoldORCiDGND |
|---|---|
| DOI: | https://doi.org/10.34645/opus-4002 |
| ISBN: | 978-3-00-074291-0 |
| Parent Title (English): | Hardware and software supporting physiological measurement (HSPM-2022), Workshop, October 27-28, 2022, Konstanz, Germany |
| Publisher: | Hochschule Reutlingen |
| Place of publication: | Reutlingen |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Publication: | 2022 |
| Release Date: | 2023/01/10 |
| Tag: | Generative Adversarial Networks; Synthetic Data; Machine Learning |
| First Page: | 23 |
| Last Page: | 25 |
| Institutes: | Institut für Angewandte Forschung - IAF |
| Open Access?: | Ja |
| Relevance: | Sonstige Publikation |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |
