  We use a contrastive self-supervised learning framework to estimate distances
to galaxies from their photometric images. We incorporate data augmentations
from computer vision as well as an application-specific augmentation accounting
for galactic dust. We find that the resulting visual representations of galaxy
images are semantically useful and allow for fast similarity searches, and can
be successfully fine-tuned for the task of redshift estimation. We show that
(1) pretraining on a large corpus of unlabeled data followed by fine-tuning on
some labels can attain the accuracy of a fully-supervised model which requires
2-4x more labeled data, and (2) that by fine-tuning our self-supervised
representations using all available data labels in the Main Galaxy Sample of
the Sloan Digital Sky Survey (SDSS), we outperform the state-of-the-art
supervised learning method.
