We conduct experiments on two different domain adaptation benchmarks, i.e. Office-31 and Office-home, to show the superior performance of our methods. Since Office-31 is a relatively small dataset, we label 30 images in each active learning stage. As for Office-home, we label 1% of unlabeled data in each stage. First, we compare with the pure-score-based sampling strategies where we directly choose samples with the highest acquisition scores (best performance highlighted in bold). The result shows that our proposed methods consistently outperform entropy, a widely used uncertainty estimator. In addition, we also modify CLUE, which is a state-of-the-art method considering both uncertainty and diversity, based on our proposed score. The model surpasses CLUE ~1% on average top-1 accuracy in the early stages, which suggests that we need to consider both classification and domain alignment in the sampling process.
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