  Active domain adaptation (ADA) studies have mainly addressed query selection
while following existing domain adaptation strategies. However, we argue that
it is critical to consider not only query selection criteria but also domain
adaptation strategies designed for ADA scenarios. This paper introduces
sequential learning considering both domain type (source/target) or labelness
(labeled/unlabeled). We first train our model only on labeled target samples
obtained by loss-based query selection. When loss-based query selection is
applied under domain shift, unuseful high-loss samples gradually increase, and
the labeled-sample diversity becomes low. To solve these, we fully utilize
pseudo labels of the unlabeled target domain by leveraging loss prediction. We
further encourage pseudo labels to have low self-entropy and diverse class
distributions. Our model significantly outperforms previous methods as well as
baseline models in various benchmark datasets.
