Radio Galaxy Classification with wGAN-Supported Augmentation Article Swipe
Janis Kummer
,
L. Rustige
,
Florian Griese
,
K. Borras
,
M. Brüggen
,
Patrick Connor
,
Frank Gaede
,
Gregor Kasieczka
,
P. Schleper
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18420/inf2022_38
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18420/inf2022_38
Novel techniques are indispensable to process the flood of data from the new generation of radio telescopes. In particular, the classification of astronomical sources in images is challenging. Morphological classification of radio galaxies could be automated with deep learning models that require large sets of labelled training data. Here, we demonstrate the use of generative models, specifically Wasserstein GANs (wGAN), to generate artificial data for different classes of radio galaxies. Subsequently, we augment the training data with images from our wGAN. We find that a simple fully-connected neural network for classification can be improved significantly by including generated images into the training set.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2206.15131
- https://arxiv.org/pdf/2206.15131
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4283775961Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18420/inf2022_38Digital Object Identifier
- Title
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Radio Galaxy Classification with wGAN-Supported AugmentationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
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2022-01-01Full publication date if available
- Authors
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Janis Kummer, L. Rustige, Florian Griese, K. Borras, M. Brüggen, Patrick Connor, Frank Gaede, Gregor Kasieczka, P. SchleperList of authors in order
- Landing page
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https://arxiv.org/abs/2206.15131Publisher landing page
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https://arxiv.org/pdf/2206.15131Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2206.15131Direct OA link when available
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Computer science, Generative grammar, Set (abstract data type), Training set, Artificial intelligence, Data set, Artificial neural network, Galaxy, Process (computing), Pattern recognition (psychology), Astrophysics, Physics, Programming language, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 1, 2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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