Unsupervised domain adaptation reduces the reliance on data annotation in
deep learning by adapting knowledge from a source to a target domain. For
privacy and efficiency concerns, source-free domain adaptation extends
unsupervised domain adaptation by adapting a pre-trained source model to an
unlabeled target domain without accessing the source data. However, most
existing source-free domain adaptation methods to date focus on the
transductive setting, where the target training set is also the testing set. In
this paper, we address source-free domain adaptation in the more realistic
inductive setting, where the target training and testing sets are mutually
exclusive. We propose a new semi-supervised fine-tuning method named Dual
Moving Average Pseudo-Labeling (DMAPL) for source-free inductive domain
adaptation. We first split the unlabeled training set in the target domain into
a pseudo-labeled confident subset and an unlabeled less-confident subset
according to the prediction confidence scores from the pre-trained source
model. Then we propose a soft-label moving-average updating strategy for the
unlabeled subset based on a moving-average prototypical classifier, which
gradually adapts the source model towards the target domain. Experiments show
that our proposed method achieves state-of-the-art performance and outperforms
previous methods by large margins.

本文提出了一种名为 DMAPL 的半监督微调方法，基于移动平均的原型分类器，用于源自由归纳域适应中的标签伪造和模型微调，并在实验证明其表现优于现有方法。