Accepted Paper: Radio Galaxy Classification at INNS DLIA 2023

Our research paper, ‘Morphological Classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs,’ has been accepted for presentation at the esteemed INNS Deep Learning Innovations and Applications (INNS DLIA 2023) workshop, which is part of the International Joint Conference on Neural Networks (IJCNN 2023). The paper will also be published in the renowned Procedia Computer Science journal!

In this study, we tackled the challenge of limited labeled data in radio galaxy classification by employing a cutting-edge semi-supervised learning approach. By harnessing the power of Group Equivariant Convolutional Neural Networks (G-CNNs) as encoders, we achieved impressive results in classifying radio galaxies into the well-known Fanaroff-Riley Type I (FRI) and Type II (FRII) categories. [Link to Paper]