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The growth in the number of galaxy images is much faster than the speed atwhich these galaxies can be labelled by humans. However, by leveraging theinformation present in the ever growing set of unlabelled images,semi-supervised learning could be an effective way of reducing the requiredlabelling and increasing classification accuracy. We develop a VariationalAutoencoder (VAE) with Equivariant Transformer layers with a classifier networkfrom the latent space. We show that this novel architecture leads toimprovements in accuracy when used for the galaxy morphology classificationtask on the Galaxy Zoo data set. In addition we show that pre-training theclassifier network as part of the VAE using the unlabelled data leads to higheraccuracy with fewer labels compared to exiting approaches. This novel VAE hasthe potential to automate galaxy morphology classification with reduced humanlabelling efforts.

Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders