  Out of the estimated few trillion galaxies, only around a million have been
detected through radio frequencies, and only a tiny fraction, approximately a
thousand, have been manually classified. We have addressed this disparity
between labeled and unlabeled images of radio galaxies by employing a
semi-supervised learning approach to classify them into the known
Fanaroff-Riley Type I (FRI) and Type II (FRII) categories. A Group Equivariant
Convolutional Neural Network (G-CNN) was used as an encoder of the
state-of-the-art self-supervised methods SimCLR (A Simple Framework for
Contrastive Learning of Visual Representations) and BYOL (Bootstrap Your Own
Latent). The G-CNN preserves the equivariance for the Euclidean Group E(2),
enabling it to effectively learn the representation of globally oriented
feature maps. After representation learning, we trained a fully-connected
classifier and fine-tuned the trained encoder with labeled data. Our findings
demonstrate that our semi-supervised approach outperforms existing
state-of-the-art methods across several metrics, including cluster quality,
convergence rate, accuracy, precision, recall, and the F1-score. Moreover,
statistical significance testing via a t-test revealed that our method
surpasses the performance of a fully supervised G-CNN. This study emphasizes
the importance of semi-supervised learning in radio galaxy classification,
where labeled data are still scarce, but the prospects for discovery are
immense.
