# Data-Efficient Classification of Radio Galaxies

arXiv: https://arxiv.org/abs/2011.13311 · HTML: https://mlsanity.com/paper/2011.13311/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [A comparative study of source-finding techniques in HI emission line cubes using SoFiA, MTObjects, and supervised deep learning](https://mlsanity.com/paper/2211.12809/) (2022)
- [Morphological Classification of Extragalactic Radio Sources Using Gradient Boosting Methods](https://mlsanity.com/paper/2304.12729/) (2023)
- [Advances on the classification of radio image cubes](https://mlsanity.com/paper/2305.03435/) (2023)
- [Deep supervised hashing for fast retrieval of radio image cubes](https://mlsanity.com/paper/2309.00932/) (2023)

## Abstract (arXiv)

The continuum emission from radio galaxies can be generally classified into different morphological classes such as FRI, FRII, Bent, or Compact. In this paper, we explore the task of radio galaxy classification based on morphology using deep learning methods with a focus on using a small scale dataset (~ 2000 samples). We apply few-shot learning techniques based on Twin Networks and transfer learning techniques using a pre-trained DenseNet model with advanced techniques like cyclical learning rate and discriminative learning to train the model rapidly. We achieve a classification accuracy of over 92% using our best performing model with the biggest source of confusion being between Bent and FRII type galaxies. Our results show that focusing on a small but curated dataset along with the use of best practices to train the neural network can lead to good results. Automated classification techniques will be crucial for upcoming surveys with next generation radio telescopes which are expected to detect hundreds of thousands of new radio galaxies in the near future.