  In this work, we examine the robustness of state-of-the-art semi-supervised
learning (SSL) algorithms when applied to morphological classification in
modern radio astronomy. We test whether SSL can achieve performance comparable
to the current supervised state of the art when using many fewer labelled data
points and if these results generalise to using truly unlabelled data. We find
that although SSL provides additional regularisation, its performance degrades
rapidly when using very few labels, and that using truly unlabelled data leads
to a significant drop in performance.
