Self-Supervised Representation Learning for Astronomical Images

• URL: http://arxiv.org/abs/2012.13083v2
• Date: Thu, 8 Apr 2021 16:06:13 GMT
• Title: Self-Supervised Representation Learning for Astronomical Images
• Authors: Md Abul Hayat, George Stein, Peter Harrington, Zarija Luki\'c, Mustafa Mustafa
• Abstract summary: Self-supervised learning recovers representations of sky survey images that are semantically useful. We show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training.
• Score: 1.0499611180329804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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