Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift

1 code implementation • 19 Apr 2022 • Inigo V. Slijepcevic, Anna M. M. Scaife, Mike Walmsley, Micah Bowles, Ivy Wong, Stanislav S. Shabala, Hongming Tang

In this work we examine the classification accuracy and robustness of a state-of-the-art semi-supervised learning (SSL) algorithm applied to the morphological classification of radio galaxies.

Benchmarking Classification