Semi-supervised domain adaptation (SSDA) aims at training a high-performance model for a target domain using few labeled target data, many unlabeled target data, and plenty of auxiliary data from a source domain. Previous works in SSDA mainly focused on learning transferable representations across domains. However, it is difficult to find a feature space where the source and target domains share the same conditional probability distribution. Additionally, there is no flexible and effective strategy extending existing unsupervised domain adaptation (UDA) approaches to SSDA settings. In order to solve the above two challenges, we propose a novel fine-tuning framework, semi-supervised transfer boosting (SS-TrBoosting). Given a well-trained deep learning-based UDA or SSDA model, we use it as the initial model, generate additional base learners by boosting, and then use all of them as an ensemble. More specifically, half of the base learners are generated by supervised domain adaptation, and half by semi-supervised learning. Furthermore, for more efficient data transmission and better data privacy protection, we propose a source data generation approach to extend SS-TrBoosting to semi-supervised source-free domain adaptation (SS-SFDA). Extensive experiments showed that SS-TrBoosting can be applied to a variety of existing UDA, SSDA and SFDA approaches to further improve their performance.

翻译：半监督域适应（SSDA）旨在利用少量带标签的目标域数据、大量无标签的目标域数据以及来自源域的大量辅助数据，为目标域训练高性能模型。先前关于SSDA的研究主要集中于学习跨域的可迁移表示。然而，找到一个使源域和目标域共享相同条件概率分布的特征空间是困难的。此外，目前缺乏一种灵活有效的策略将现有的无监督域适应（UDA）方法扩展到SSDA场景。为解决上述两个挑战，我们提出了一种新颖的微调框架——半监督迁移提升（SS-TrBoosting）。给定一个训练良好的基于深度学习的UDA或SSDA模型，我们将其作为初始模型，通过提升方法生成额外的基学习器，然后将它们全部集成使用。具体而言，一半的基学习器通过监督域适应生成，另一半通过半监督学习生成。此外，为了实现更高效的数据传输和更好的数据隐私保护，我们提出了一种源数据生成方法，将SS-TrBoosting扩展至半监督无源域适应（SS-SFDA）。大量实验表明，SS-TrBoosting可应用于多种现有的UDA、SSDA和SFDA方法，进一步提升其性能。