对比学习在天体物理学中的应用综述

Marc Huertas-Company, Regina Sarmiento, Johan H Knapen
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引用次数: 0

摘要

随着天文数据集的体积和复杂性的增加,从高维空间中提取模式的可靠工具变得越来越必要。对比学习是一种自监督机器学习算法,它从多维数据集中提取信息测量,近年来在计算机视觉和机器学习社区越来越流行。为此,它最大化了从相同输入数据的扩充版本中提取的信息之间的一致性,使最终表示对于应用的转换保持不变。对比学习在天文学中特别有用,可以去除已知的工具效应,并使用有限数量的可用标签执行监督分类和回归,这显示了基础模型的有前途的途径。这篇简短的综述文章简要地总结了对比学习背后的主要概念,并回顾了对比学习在天文学中的第一个有前途的应用。我们包含了一些实用的建议,这些建议对对比学习特别有吸引力。
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A brief review of contrastive learning applied to astrophysics
Abstract Reliable tools to extract patterns from high-dimensionality spaces are becoming more necessary as astronomical data sets increase both in volume and complexity. Contrastive Learning is a self-supervised machine learning algorithm that extracts informative measurements from multidimensional data sets, which has become increasingly popular in the computer vision and Machine Learning communities in recent years. To do so, it maximizes the agreement between the information extracted from augmented versions of the same input data, making the final representation invariant to the applied transformations. Contrastive Learning is particularly useful in astronomy for removing known instrumental effects and for performing supervised classifications and regressions with a limited amount of available labels, showing a promising avenue towards Foundation Models. This short review paper briefly summarizes the main concepts behind contrastive learning and reviews the first promising applications to astronomy. We include some practical recommendations on which applications are particularly attractive for contrastive learning.
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