Semi-supervised domain adaptation (SSDA) is to adapt a learner to a new
domain with only a small set of labeled samples when a large labeled dataset is
given on a source domain. In this paper, we propose a pair-based SSDA method
that adapts a model to the target domain using self-distillation with sample
pairs. Each sample pair is composed of a teacher sample from a labeled dataset
(i.e., source or labeled target) and its student sample from an unlabeled
dataset (i.e., unlabeled target). Our method generates an assistant feature by
transferring an intermediate style between the teacher and the student, and
then train the model by minimizing the output discrepancy between the student
and the assistant. During training, the assistants gradually bridge the
discrepancy between the two domains, thus allowing the student to easily learn
from the teacher. Experimental evaluation on standard benchmarks shows that our
method effectively minimizes both the inter-domain and intra-domain
discrepancies, thus achieving significant improvements over recent methods.

本研究提出了一种基于样本对的半监督领域自适应方法，该方法通过自蒸馏生成辅助特征，并通过最小化学生和辅助之间的输出差异来训练模型以减少领域之间和领域内的差异，在标准基准上实验结果表明该方法有效地减少了差异，并取得了明显的改进。

基于样本自我蒸馏的半监督域自适应

Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation

Although unsupervised domain adaptation methods have been widely adopted
across several computer vision tasks, it is more desirable if we can exploit a
few labeled data from new domains encountered in a real application. The novel
setting of the semi-supervised domain adaptation (SSDA) problem shares the
challenges with the domain adaptation problem and the semi-supervised learning
problem. However, a recent study shows that conventional domain adaptation and
semi-supervised learning methods often result in less effective or negative
transfer in the SSDA problem. In order to interpret the observation and address
the SSDA problem, in this paper, we raise the intra-domain discrepancy issue
within the target domain, which has never been discussed so far. Then, we
demonstrate that addressing the intra-domain discrepancy leads to the ultimate
goal of the SSDA problem. We propose an SSDA framework that aims to align
features via alleviation of the intra-domain discrepancy. Our framework mainly
consists of three schemes, i.e., attraction, perturbation, and exploration.
First, the attraction scheme globally minimizes the intra-domain discrepancy
within the target domain. Second, we demonstrate the incompatibility of the
conventional adversarial perturbation methods with SSDA. Then, we present a
domain adaptive adversarial perturbation scheme, which perturbs the given
target samples in a way that reduces the intra-domain discrepancy. Finally, the
exploration scheme locally aligns features in a class-wise manner complementary
to the attraction scheme by selectively aligning unlabeled target features
complementary to the perturbation scheme. We conduct extensive experiments on
domain adaptation benchmark datasets such as DomainNet, Office-Home, and
Office. Our method achieves state-of-the-art performances on all datasets.

该论文提出了一种半监督领域自适应框架（SSDA），主要通过三个方案（吸引、扰动和探索）来解决目标域中的域内差异问题，进而实现特征对齐。在多个数据集上的实验表明，该方法优于其他方法。