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.

本文介绍了一种名为DeepAstroUDA的通用领域自适应方法，可用于具有不同数据分布和类别重叠的数据集，以及存在未知类的情况。我们将其应用于三个不同复杂度的星系形态分类任务的示例中，成功使用领域自适应方法实现了极度不一致的观测数据的分类，提高了模型性能，使模型能够在多个数据集上实现一致的表现。同时，我们的方法也用作了一种异常检测算法，并可以成功地聚类未知类样本。

DeepAstroUDA: 跨数据收集半监督通用领域自适应，用于星系形态分类和异常检测