A brief review of contrastive learning applied to astrophysics
This is an incremental review paper summarizing existing methods for astronomers dealing with large, complex datasets.
The paper reviews contrastive learning as a self-supervised method for extracting patterns from high-dimensional astronomical data, highlighting its utility in removing instrumental effects and enabling supervised tasks with limited labels, though it does not present new experimental results or concrete numbers.
Reliable tools to extract patterns from high-dimensionality spaces are becoming more necessary as astronomical datasets increase both in volume and complexity. Contrastive Learning is a self-supervised machine learning algorithm that extracts informative measurements from multi-dimensional datasets, 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 \emph{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.