Attention-gating for improved radio galaxy classification
  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.
