论文标题
天文图像分类的自学学习
Self-supervised Learning for Astronomical Image Classification
论文作者
论文摘要
在天文学中，每天通过光度测量生成大量图像数据，该调查扫描天空以从星星，星系和其他天体物体中收集数据。在本文中，我们提出了一种技术来利用未标记的天文图像来预先卷积的卷积神经网络，以学习特定于域的特征提取器，从而改善了具有少量标记数据的设置中机器学习技术的结果。我们表明，我们的技术产生的结果在许多情况下比使用ImageNet预训练要好。
In Astronomy, a huge amount of image data is generated daily by photometric surveys, which scan the sky to collect data from stars, galaxies and other celestial objects. In this paper, we propose a technique to leverage unlabeled astronomical images to pre-train deep convolutional neural networks, in order to learn a domain-specific feature extractor which improves the results of machine learning techniques in setups with small amounts of labeled data available. We show that our technique produces results which are in many cases better than using ImageNet pre-training.
