  In Astronomy, a huge amount of image data is generated daily by photometric
surveys, which scan the sky to collect data from stars, galaxies and other
celestial objects. In this paper, we propose a technique to leverage unlabeled
astronomical images to pre-train deep convolutional neural networks, in order
to learn a domain-specific feature extractor which improves the results of
machine learning techniques in setups with small amounts of labeled data
available. We show that our technique produces results which are in many cases
better than using ImageNet pre-training.
