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

arXiv: https://arxiv.org/abs/2111.04357 · HTML: https://mlsanity.com/paper/2111.04357/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [A Theory of Label Propagation for Subpopulation Shift](https://mlsanity.com/paper/2102.11203/) (2021)
- [Attention-gating for improved radio galaxy classification](https://mlsanity.com/paper/2012.01248/) (2020)
- [Domain adaptation techniques for improved cross-domain study of galaxy mergers](https://mlsanity.com/paper/2011.03591/) (2020)

## Abstract (arXiv)

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.