  Artificial intelligence methods show great promise in increasing the quality
and speed of work with large astronomical datasets, but the high complexity of
these methods leads to the extraction of dataset-specific, non-robust features.
Therefore, such methods do not generalize well across multiple datasets. We
present a universal domain adaptation method, \textit{DeepAstroUDA}, as an
approach to overcome this challenge. This algorithm performs semi-supervised
domain adaptation and can be applied to datasets with different data
distributions and class overlaps. Non-overlapping classes can be present in any
of the two datasets (the labeled source domain, or the unlabeled target
domain), and the method can even be used in the presence of unknown classes. We
apply our method to three examples of galaxy morphology classification tasks of
different complexities ($3$-class and $10$-class problems), with anomaly
detection: 1) datasets created after different numbers of observing years from
a single survey (LSST mock data of $1$ and $10$ years of observations); 2) data
from different surveys (SDSS and DECaLS); and 3) data from observing fields
with different depths within one survey (wide field and Stripe 82 deep field of
SDSS). For the first time, we demonstrate the successful use of domain
adaptation between very discrepant observational datasets.
\textit{DeepAstroUDA} is capable of bridging the gap between two astronomical
surveys, increasing classification accuracy in both domains (up to $40\%$ on
the unlabeled data), and making model performance consistent across datasets.
Furthermore, our method also performs well as an anomaly detection algorithm
and successfully clusters unknown class samples even in the unlabeled target
dataset.
