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Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation

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Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation. / Rustige, Lennart; Kummer, Janis; Griese, Florian; Borras, Kerstin; Brüggen, Marcus; Connor, Patrick; Gaede, Frank; Kasiecka, Gregor; Knopp, Tobias; Schleper, Peter.

In: RAS Techniques and Instruments, Vol. 2, No. 1, 26.05.2023, p. 264–277.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Rustige, L, Kummer, J, Griese, F, Borras, K, Brüggen, M, Connor, P, Gaede, F, Kasiecka, G, Knopp, T & Schleper, P 2023, 'Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation', RAS Techniques and Instruments, vol. 2, no. 1, pp. 264–277. https://doi.org/10.1093/rasti/rzad016

APA

Rustige, L., Kummer, J., Griese, F., Borras, K., Brüggen, M., Connor, P., Gaede, F., Kasiecka, G., Knopp, T., & Schleper, P. (2023). Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation. RAS Techniques and Instruments, 2(1), 264–277. https://doi.org/10.1093/rasti/rzad016

Vancouver

Rustige L, Kummer J, Griese F, Borras K, Brüggen M, Connor P et al. Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation. RAS Techniques and Instruments. 2023 May 26;2(1):264–277. https://doi.org/10.1093/rasti/rzad016

Bibtex

@article{1a31ee600f554461b3224e773e7cf083,
title = "Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation",
abstract = "Machine learning techniques that perform morphological classification of astronomical sources often suffer from a scarcity of labelled training data. Here, we focus on the case of supervised deep learning models for the morphological classification of radio galaxies, which is particularly topical for the forthcoming large radio surveys. We demonstrate the use of generative models, specifically Wasserstein generative adversarial networks (wGANs), to generate data for different classes of radio galaxies. Further, we study the impact of augmenting the training data with images from our wGAN on three different classification architectures. We find that this technique makes it possible to improve models for the morphological classification of radio galaxies. A simple fully connected neural network benefits most from including generated images into the training set, with a considerable improvement of its classification accuracy. In addition, we find it is more difficult to improve complex classifiers. The classification performance of a convolutional neural network can be improved slightly. However, this is not the case for a vision transformer.",
author = "Lennart Rustige and Janis Kummer and Florian Griese and Kerstin Borras and Marcus Br{\"u}ggen and Patrick Connor and Frank Gaede and Gregor Kasiecka and Tobias Knopp and Peter Schleper",
year = "2023",
month = may,
day = "26",
doi = "10.1093/rasti/rzad016",
language = "English",
volume = "2",
pages = "264–277",
number = "1",

}

RIS

TY - JOUR

T1 - Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation

AU - Rustige, Lennart

AU - Kummer, Janis

AU - Griese, Florian

AU - Borras, Kerstin

AU - Brüggen, Marcus

AU - Connor, Patrick

AU - Gaede, Frank

AU - Kasiecka, Gregor

AU - Knopp, Tobias

AU - Schleper, Peter

PY - 2023/5/26

Y1 - 2023/5/26

N2 - Machine learning techniques that perform morphological classification of astronomical sources often suffer from a scarcity of labelled training data. Here, we focus on the case of supervised deep learning models for the morphological classification of radio galaxies, which is particularly topical for the forthcoming large radio surveys. We demonstrate the use of generative models, specifically Wasserstein generative adversarial networks (wGANs), to generate data for different classes of radio galaxies. Further, we study the impact of augmenting the training data with images from our wGAN on three different classification architectures. We find that this technique makes it possible to improve models for the morphological classification of radio galaxies. A simple fully connected neural network benefits most from including generated images into the training set, with a considerable improvement of its classification accuracy. In addition, we find it is more difficult to improve complex classifiers. The classification performance of a convolutional neural network can be improved slightly. However, this is not the case for a vision transformer.

AB - Machine learning techniques that perform morphological classification of astronomical sources often suffer from a scarcity of labelled training data. Here, we focus on the case of supervised deep learning models for the morphological classification of radio galaxies, which is particularly topical for the forthcoming large radio surveys. We demonstrate the use of generative models, specifically Wasserstein generative adversarial networks (wGANs), to generate data for different classes of radio galaxies. Further, we study the impact of augmenting the training data with images from our wGAN on three different classification architectures. We find that this technique makes it possible to improve models for the morphological classification of radio galaxies. A simple fully connected neural network benefits most from including generated images into the training set, with a considerable improvement of its classification accuracy. In addition, we find it is more difficult to improve complex classifiers. The classification performance of a convolutional neural network can be improved slightly. However, this is not the case for a vision transformer.

U2 - 10.1093/rasti/rzad016

DO - 10.1093/rasti/rzad016

M3 - SCORING: Journal article

VL - 2

SP - 264

EP - 277

IS - 1

ER -