  The growth in the number of galaxy images is much faster than the speed at
which these galaxies can be labelled by humans. However, by leveraging the
information present in the ever growing set of unlabelled images,
semi-supervised learning could be an effective way of reducing the required
labelling and increasing classification accuracy. We develop a Variational
Autoencoder (VAE) with Equivariant Transformer layers with a classifier network
from the latent space. We show that this novel architecture leads to
improvements in accuracy when used for the galaxy morphology classification
task on the Galaxy Zoo data set. In addition we show that pre-training the
classifier network as part of the VAE using the unlabelled data leads to higher
accuracy with fewer labels compared to exiting approaches. This novel VAE has
the potential to automate galaxy morphology classification with reduced human
labelling efforts.
