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In this work, we examine the robustness of state-of-the-art semi-supervisedlearning (SSL) algorithms when applied to morphological classification inmodern radio astronomy. We test whether SSL can achieve performance comparableto the current supervised state of the art when using many fewer labelled datapoints and if these results generalise to using truly unlabelled data. We findthat although SSL provides additional regularisation, its performance degradesrapidly when using very few labels, and that using truly unlabelled data leadsto a significant drop in performance.

Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?