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 of HS Reutlingen | Martínez 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): |  Creative Commons - CC BY - Namensnennung 4.0 International |
