  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.
