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- W4283775961 abstract "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." @default.
- W4283775961 created "2022-07-03" @default.
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- W4283775961 date "2022-06-30" @default.
- W4283775961 modified "2023-10-13" @default.
- W4283775961 title "Radio Galaxy Classification with wGAN-Supported Augmentation" @default.
- W4283775961 doi "https://doi.org/10.18420/inf2022_38" @default.
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