  Black-Box unsupervised domain adaptation (BBUDA) learns knowledge only with
the prediction of target data from the source model without access to the
source data and source model, which attempts to alleviate concerns about the
privacy and security of data. However, incorrect pseudo-labels are prevalent in
the prediction generated by the source model due to the cross-domain
discrepancy, which may substantially degrade the performance of the target
model. To address this problem, we propose a novel approach that incrementally
selects high-confidence pseudo-labels to improve the generalization ability of
the target model. Specifically, we first generate pseudo-labels using a source
model and train a crude target model by a vanilla BBUDA method. Second, we
iteratively select high-confidence data from the low-confidence data pool by
thresholding the softmax probabilities, prototype labels, and intra-class
similarity. Then, we iteratively train a stronger target network based on the
crude target model to correct the wrongly labeled samples to improve the
accuracy of the pseudo-label. Experimental results demonstrate that the
proposed method achieves state-of-the-art black-box unsupervised domain
adaptation performance on three benchmark datasets.
