Sky surveys are the largest data generators in astronomy, making automated
tools for extracting meaningful scientific information an absolute necessity.
We show that, without the need for labels, self-supervised learning recovers
representations of sky survey images that are semantically useful for a variety
of scientific tasks. These representations can be directly used as features, or
fine-tuned, to outperform supervised methods trained only on labeled data. We
apply a contrastive learning framework on multi-band galaxy photometry from the
Sloan Digital Sky Survey (SDSS) to learn image representations. We then use
them for galaxy morphology classification, and fine-tune them for photometric
redshift estimation, using labels from the Galaxy Zoo 2 dataset and SDSS
spectroscopy. In both downstream tasks, using the same learned representations,
we outperform the supervised state-of-the-art results, and we show that our
approach can achieve the accuracy of supervised models while using 2-4 times
fewer labels for training.

使用自监督学习的方式学习天文学中的巨大数据源，通过图像表示的学习，可以在不需要过多标记的情况下获得与使用有监督学习方法获得的结果相当甚至更好的效果，可能可以有效减少标记数量。