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Authors: Ana Martinazzo ; Mateus Espadoto and Nina S. T. Hirata

Affiliation: Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil

Keyword(s): Deep Learning, Astronomy, Galaxy Morphology, Merging Galaxies, Transfer Learning, Neural Networks, ImageNet.

Abstract: With the emergence of photometric surveys in astronomy, came the challenge of processing and understanding an enormous amount of image data. In this paper, we systematically compare the performance of five popular ConvNet architectures when applied to three different image classification problems in astronomy to determine which architecture works best for each problem. We show that a VGG-style architecture pre-trained on ImageNet yields the best results on all studied problems, even when compared to architectures which perform much better on the ImageNet competition.

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Paper citation in several formats:
Martinazzo, A.; Espadoto, M. and Hirata, N. (2020). Deep Learning for Astronomical Object Classification: A Case Study. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 87-95. DOI: 10.5220/0008939800870095

@conference{visapp20,
author={Ana Martinazzo. and Mateus Espadoto. and Nina S. T. Hirata.},
title={Deep Learning for Astronomical Object Classification: A Case Study},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={87-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008939800870095},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Deep Learning for Astronomical Object Classification: A Case Study
SN - 978-989-758-402-2
IS - 2184-4321
AU - Martinazzo, A.
AU - Espadoto, M.
AU - Hirata, N.
PY - 2020
SP - 87
EP - 95
DO - 10.5220/0008939800870095
PB - SciTePress

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