Attention-gating for improved radio galaxy classification (2012.01248v2)

Abstract: 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.

• The paper introduces an attention-gated CNN that reduces parameters by over 50% while achieving 84% classification accuracy.
• It employs attention mechanisms to differentiate FRI and FRII classes, with F1 scores of 0.82 and 0.86 respectively.
• Attention maps enhance model interpretability by highlighting image regions aligned with expert observations.

Attention-Gating for Improved Radio Galaxy Classification

The paper "Attention-gating for Improved Radio Galaxy Classification" presents an innovative approach to the classification of radio galaxies employing convolutional neural networks (CNNs) enhanced by attention mechanisms. In recent years, the vast influx of astronomical data facilitated by advanced radio telescopes has compelled the adoption of machine learning techniques for efficient data analysis and classification tasks. This paper introduces an attention-gated CNN model specifically tailored for the classification of radio galaxies, which reduces the parameter count by over 50% compared to traditional CNN models in this domain, without compromising performance.

The primary contribution of this paper is the incorporation of attention-gating in CNNs aimed at identifying and differentiating between Fanaroff-Riley Class I (FRI) and Class II (FRII) radio galaxies. The model's architecture draws inspiration from existing models used in medical imaging, which have shown success in managing high complexity visual data with reduced learnable parameters. By incorporating attention mechanisms, the model not only achieves competitive accuracy in classification but also allows for greater interpretability through attention maps indicating which parts of the radio image influenced the model's decision.

The results presented in the paper show that the attention-gated CNN model performs comparably to the traditional CNN models, which have been the standard in radio galaxy classification thus far. The model trained with the MiraBest data set demonstrates an accuracy of 84% in differentiating between FRI and FRII sources, with class-specific F1 scores of 0.82 and 0.86 for the FRI and FRII classes, respectively. Importantly, the attention mechanism elucidates the features considered by the network, aligning well with the regions identified by human experts, thus providing a dual benefit of performance and transparency.

The implications of this research are multifaceted. Practically, the model enables the processing and classification of large astronomical data sets more efficiently due to its reduced computational demands. Theoretically, the integration of attention into CNNs offers new pathways for enhancing interpretability and opens up avenues for further refinement in classification models by distinctly presenting how models arrive at specific classifications. This interpretability can drive better trust and understanding in machine learning outputs, facilitating their broader acceptance in scientific research.

Future developments could focus on further optimizing normalization and aggregation methods in attention-gating to refine model interpretability without sacrificing accuracy. Moreover, expanding the model to include multi-class classification or integrating data from multiple wavebands could provide a more holistic understanding of radio galaxy features across the electromagnetic spectrum.

In conclusion, this paper advances the field of radio galaxy classification by integrating an attention-gating mechanism into convolutional neural networks, offering both reduced computational complexity and increased interpretability. This dual achievement underscores the potential for attention mechanisms to transform astrophysical data analysis and paves the way for continued research into more efficient and transparent classification models.