  In the era of big astronomical surveys, our ability to leverage artificial
intelligence algorithms simultaneously for multiple datasets will open new
avenues for scientific discovery. Unfortunately, simply training a deep neural
network on images from one data domain often leads to very poor performance on
any other dataset. Here we develop a Universal Domain Adaptation method
DeepAstroUDA, capable of performing semi-supervised domain alignment that can
be applied to datasets with different types of class overlap. Extra classes can
be present in any of the two datasets, and the method can even be used in the
presence of unknown classes. For the first time, we demonstrate the successful
use of domain adaptation on two very different observational datasets (from
SDSS and DECaLS). We show that our method is capable of bridging the gap
between two astronomical surveys, and also performs well for anomaly detection
and clustering of unknown data in the unlabeled dataset. We apply our model to
two examples of galaxy morphology classification tasks with anomaly detection:
1) classifying spiral and elliptical galaxies with detection of merging
galaxies (three classes including one unknown anomaly class); 2) a more
granular problem where the classes describe more detailed morphological
properties of galaxies, with the detection of gravitational lenses (ten classes
including one unknown anomaly class).
