  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 GANs (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 (FCN) 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
(CNN) can be improved slightly. However, this is not the case for a Vision
Transformer (ViT).
