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- Document type:
- Forschungsdaten
- Publication date:
- 12.07.2022
- Responsible:
- Benjamin J. Holzschuh
- Authors:
- Benjamin J. Holzschuh; Conor M. O’Riordan; Simona Vegetti; Vicente Rodriguez-Gomez; Nils Thuerey
- Author affiliation:
- TUM
- Publisher:
- TUM
- Title:
- AI Generated Galaxy Images
- Identifier:
- doi:10.14459/2022mp1661654
- End date of data production:
- 24.03.2022
- Subject area:
- DAT Datenverarbeitung, Informatik; PHY Physik
- Other subject areas:
- Astronomy, Astrophysics, Artificial Intelligence, Image processing, Computer Vision
- Resource type:
- Simulationen / simulations; Abbildungen von Objekten / image of objects
- Data type:
- Bilder / images; Datenbanken / data bases
- Description:
- Datasets of AI generated galaxy images based on simulations (TNG-Illustris), observations (COSMOS) and analytic expressions (Sérsic profiles)
- Method of data assessment:
- Recent generative models were trained on datasets of galaxy images. The available datasets consist of galaxy images sampled from a StyleGAN-like model.
- Links:
- Key words:
- Machine Learning, Astrophysics, Cosmology
- Technical remarks:
- View and download (51 GB total, 4 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1661654):
rsync rsync://m1661654@dataserv.ub.tum.de/m1661654/ - Language:
- en
- Rights:
- by, http://creativecommons.org/licenses/by/4.0
- Horizon 2020:
- ERC Consolidator Grant SpaTe (CoG-2019-863850), European Union’s Horizon 2020 research and innovation pro-gramme (LEDA: grant agree-ment no. 758853)
BibTeX