天文图像分类的自我监督学习技术（CS CV）---刘持诚

在天文学中，每天都有大量的图像数据通过光度测量调查产生。光度测量通过扫描天空来收集恒星、星系和其他天体的数据。在本文中，我们提出了一种技术，利用未标记的天文图像来预训练深度卷积神经网络，以学习一种特定领域的特征提取器，从而提高了机器学习技术在有少量标记数据的情况下的训练结果。我们发现，这项技术产生的结果在许多情况下比使用 ImageNet 预训练的结果要好。

原文题目：Self-supervised Learning for Astronomical Image Classification

原文：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.

原文作者：Ana Martinazzo, Mateus Espadoto, Nina S. T. Hirata

原文地址：https://arxiv.org/abs/2004.11336