Attention-gating for improved radio galaxy classification
This work offers a more efficient and interpretable method for astronomers to classify radio galaxies, potentially aiding in large-scale astrophysical surveys.
This paper introduces an attention-based model for classifying radio galaxies, achieving performance comparable to existing classifiers while using over 50% fewer parameters than the next smallest classic CNN. The study also explores how normalization and aggregation methods in attention-gating influence model output and interpretability.
In this work we introduce attention as a state of the art mechanism for classification of radio galaxies using convolutional neural networks. We present an attention-based model that performs on par with previous classifiers while using more than 50% fewer parameters than the next smallest classic CNN application in this field. We demonstrate quantitatively how the selection of normalisation and aggregation methods used in attention-gating can affect the output of individual models, and show that the resulting attention maps can be used to interpret the classification choices made by the model. We observe that the salient regions identified by the our model align well with the regions an expert human classifier would attend to make equivalent classifications. We show that while the selection of normalisation and aggregation may only minimally affect the performance of individual models, it can significantly affect the interpretability of the respective attention maps and by selecting a model which aligns well with how astronomers classify radio sources by eye, a user can employ the model in a more effective manner.