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.

翻译：在这项工作中,我们把注意力作为利用进化神经网络对无线电星系进行分类的最先进机制。我们提出了一个关注模型,它与以前的分类系统一样,使用比下一个最小的经典有线电视新闻网在这一领域应用最微小的经典有线电视新闻网应用软件少50个或更少的参数。我们从数量上展示了在关注引线中采用的正常化和汇总方法的选择会如何影响单个模型的输出,并表明由此产生的关注地图可以用来解释模型作出的分类选择。我们注意到,我们模型所查明的突出区域与一个人类分类专家所参加的区域非常吻合,可以进行同等分类。我们表明,虽然正常化和汇总的选择只能对单个模型的性能产生最小影响,但是它能够极大地影响相关关注地图的可解释性,并且通过选择一个模型来与天文仪对无线电源进行眼睛分类的方式保持一致,用户可以更有效地使用该模型。