用深度残差与卷积网络分类星系，准确率超90%
Classifying galaxies in the Galaxy10 DECals dataset using Inception and Residual CNNs
- 采用残差连接和并行模块设计深层网络，提升计算效率
- 在银河10数据集上达到约90%准确率，残差网络表现更优
- 适合未来天文巡天图像分类任务，可作为基础模型
未来几年，星系形态图像的数据量和质量将显著提升，探索适用于图像分类任务的高效深度学习架构至关重要。残差网络（ResNet）与Inception网络因其计算效率高，通过残差连接和并行化Inception模块实现更深网络而不显著增加复杂度，是理想研究对象。本文在空间增强的Galaxy10 DECals数据集上评估了ResNet101与InceptionV4的表现。保持十类星系分类不变，调整各类图像数量后发现，两者均达到约90%的准确率，与文献报道相当。性能指标显示，ResNet101优于InceptionV4。结果表明，这两种CNN架构均可作为未来巡天星系图像分类专用流程的可靠基础。
原文摘要 · Abstract (English)
Image data regarding galactic morphology is expected to increase both in quantity and quality for the next foreseeable years; thus it is important to explore which deep learning architectures adapted for image classification tasks are cost-effective. Residual and Inception networks are ideal for exploring classification convolutional neural networks (CNNs) due to their computational efficiency, achieved through techniques such as residual connections and parallelized inception modules, enabling deeper networks without excessively increasing computational complexity. In this work, we analyze the performance of ResNet101 and InceptionV4 on a spatially-augmented Galaxy10 DECals dataset. Retaining the ten-class classification of galaxies, we modify the image count of each class. We find that ResNet101 and InceptionV4 models achieved accuracies of $\sim$ 90%, comparable with reported performance in the literature. In terms of performance metrics, ResNet101 is superior to InceptionV4. Our results indicate that either of these CNN architectures could serve as a robust foundation for specialized pipelines for classification of galaxy images from upcoming surveys.
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