  Domain adaptation (DA) mitigates the domain shift problem when transferring
knowledge from one annotated domain to another similar but different unlabeled
domain. However, existing models often utilize one of the ImageNet models as
the backbone without exploring others, and fine-tuning or retraining the
backbone ImageNet model is also time-consuming. Moreover, pseudo-labeling has
been used to improve the performance in the target domain, while how to
generate confident pseudo labels and explicitly align domain distributions has
not been well addressed. In this paper, we show how to efficiently opt for the
best pre-trained features from seventeen well-known ImageNet models in
unsupervised DA problems. In addition, we propose a recurrent pseudo-labeling
model using the best pre-trained features (termed PRPL) to improve
classification performance. To show the effectiveness of PRPL, we evaluate it
on three benchmark datasets, Office+Caltech-10, Office-31, and Office-Home.
Extensive experiments show that our model reduces computation time and boosts
the mean accuracy to 98.1%, 92.4%, and 81.2%, respectively, substantially
outperforming the state of the art.
