Unsupervised domain adaptation (UDA) has achieved great success in handling cross-domain machine learning applications. It typically benefits the model training of unlabeled target domain by leveraging knowledge from labeled source domain. For this purpose, the minimization of the marginal distribution divergence and conditional distribution divergence between the source and the target domain is widely adopted in existing work. Nevertheless, for the sake of privacy preservation, the source domain is usually not provided with training data but trained predictor (e.g., classifier). This incurs the above studies infeasible because the marginal and conditional distributions of the source domain are incalculable. To this end, this article proposes a source-free UDA which jointly models domain adaptation and sample transport learning, namely Sample Transport Domain Adaptation (STDA). Specifically, STDA constructs the pseudo source domain according to the aggregated decision boundaries of multiple source classifiers made on the target domain. Then, it refines the pseudo source domain by augmenting it through transporting those target samples with high confidence, and consequently generates labels for the target domain. We train the STDA model by performing domain adaptation with sample transport between the above steps in alternating manner, and eventually achieve knowledge adaptation to the target domain and attain confident labels for it. Finally, evaluation results have validated effectiveness and superiority of the proposed method.

无监督域适应（UDA）在处理跨域机器学习应用方面取得了巨大成功。通过利用来自标记源域的知识，它通常有利于未标记目标域的模型训练。为此，在现有工作中广泛采用最小化源域和目标域之间的边际分布散度和条件分布散度。尽管如此，为了隐私保护，源域通常不提供训练数据，而是提供训练好的预测器（例如分类器）。这导致上述研究不可行，因为源域的边缘和条件分布是不可计算的。为此，本文提出了一种无源 UDA，它联合建模领域适应和样本传输学习，即样本传输域适应（STDA）。具体来说，STDA 根据多个源分类器在目标域上的聚合决策边界构造伪源域。然后，它通过以高置信度传输这些目标样本来增强伪源域来细化伪源域，从而为目标域生成标签。我们通过在上述步骤之间以交替方式执行域适应和样本传输来训练 STDA 模型，最终实现对目标域的知识适应并为其获得置信标签。最后，评估结果验证了所提出方法的有效性和优越性。STDA 根据多个源分类器在目标域上的聚合决策边界构造伪源域。然后，它通过以高置信度传输这些目标样本来增强伪源域来细化伪源域，从而为目标域生成标签。我们通过在上述步骤之间以交替方式执行域适应和样本传输来训练 STDA 模型，最终实现对目标域的知识适应并为其获得置信标签。最后，评估结果验证了所提出方法的有效性和优越性。STDA 根据多个源分类器在目标域上的聚合决策边界构造伪源域。然后，它通过以高置信度传输这些目标样本来增强伪源域来细化伪源域，从而为目标域生成标签。我们通过在上述步骤之间以交替方式执行域适应和样本传输来训练 STDA 模型，最终实现对目标域的知识适应并为其获得置信标签。最后，评估结果验证了所提出方法的有效性和优越性。并因此为目标域生成标签。我们通过在上述步骤之间以交替方式执行域适应和样本传输来训练 STDA 模型，最终实现对目标域的知识适应并为其获得置信标签。最后，评估结果验证了所提出方法的有效性和优越性。并因此为目标域生成标签。我们通过在上述步骤之间以交替方式执行域适应和样本传输来训练 STDA 模型，最终实现对目标域的知识适应并为其获得置信标签。最后，评估结果验证了所提出方法的有效性和优越性。