Predicting galaxy morphology using attention-enhanced ResNets

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Earth Science Informatics Pub Date : 2024-08-24 DOI:10.1007/s12145-024-01449-6
Akshit Gupta, Kanwarpreet Kaur, Neeru Jindal
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Abstract

The practice of categorizing the galaxies according to morphologies exists and offers crucial details on the creation and development of the universe. The conventional visual inspection techniques have been very subjective and time-consuming. However, it is now possible to classify galaxies with greater accuracy owing to advancements in deep learning techniques. Deep Learning has demonstrated considerable potential in the research of galaxy classification and offers fresh perspectives on the genesis and evolution of galaxies. The suggested methodology employs Residual Networks for variety in a transfer learning-based method. To improve the accuracy of ResNet, an attention mechanism has been included. In our investigation, we used two relatively shallow ResNet models, ResNet18 and ResNet50 by incorporating a soft attention mechanism into them. The presented approach is validated on the Galaxy Zoo dataset from Kaggle. The accuracy increases from 60.15% to 80.20% for ResNet18 and from 78.21% to 80.55% for ResNet50, thus, demonstrating that the proposed work is now on a level with the accuracy of the far more complex, ResNet152 model. We have found that the attention mechanism can successfully improve the accuracy of even shallow models, which has implications for future studies in image recognition tasks.

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利用注意力增强型 ResNets 预测星系形态
根据形态对星系进行分类的做法是存在的,它提供了有关宇宙产生和发展的重要细节。传统的视觉检测技术非常主观且耗时。然而,由于深度学习技术的进步,现在可以更准确地对星系进行分类。深度学习已在星系分类研究中展现出相当大的潜力,并为星系的起源和演化提供了全新的视角。所建议的方法采用基于迁移学习的残差网络(Residual Networks)进行分类。为了提高残差网络的准确性,还加入了注意力机制。在研究中,我们使用了两个相对较浅的 ResNet 模型 ResNet18 和 ResNet50,并在其中加入了软注意力机制。所提出的方法在 Kaggle 的 Galaxy Zoo 数据集上进行了验证。ResNet18的准确率从60.15%提高到80.20%,ResNet50的准确率从78.21%提高到80.55%,这表明所提出的方法与更为复杂的ResNet152模型的准确率相当。我们发现,即使是浅层模型,注意力机制也能成功提高其准确性,这对未来图像识别任务的研究具有重要意义。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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