Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?
This addresses labeling efficiency for astronomers, but results are incremental as they show limitations rather than breakthroughs.
The study investigated whether semi-supervised learning could reduce manual labeling for radio galaxy morphology classification, finding that performance degrades rapidly with very few labels and drops significantly with unlabeled data.
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.