Compared to unsupervised domain adaptation, semi-supervised domain adaptation
(SSDA) aims to significantly improve the classification performance and
generalization capability of the model by leveraging the presence of a small
amount of labeled data from the target domain. Several SSDA approaches have
been developed to enable semantic-aligned feature confusion between labeled (or
pseudo labeled) samples across domains; nevertheless, owing to the scarcity of
semantic label information of the target domain, they were arduous to fully
realize their potential. In this study, we propose a novel SSDA approach named
Graph-based Adaptive Betweenness Clustering (G-ABC) for achieving categorical
domain alignment, which enables cross-domain semantic alignment by mandating
semantic transfer from labeled data of both the source and target domains to
unlabeled target samples. In particular, a heterogeneous graph is initially
constructed to reflect the pairwise relationships between labeled samples from
both domains and unlabeled ones of the target domain. Then, to degrade the
noisy connectivity in the graph, connectivity refinement is conducted by
introducing two strategies, namely Confidence Uncertainty based Node Removal
and Prediction Dissimilarity based Edge Pruning. Once the graph has been
refined, Adaptive Betweenness Clustering is introduced to facilitate semantic
transfer by using across-domain betweenness clustering and within-domain
betweenness clustering, thereby propagating semantic label information from
labeled samples across domains to unlabeled target data. Extensive experiments
on three standard benchmark datasets, namely DomainNet, Office-Home, and
Office-31, indicated that our method outperforms previous state-of-the-art SSDA
approaches, demonstrating the superiority of the proposed G-ABC algorithm.

提出了一种名为 G-ABC 的新型 SSDA 方法，该方法通过使用源域和目标域的标记数据对未标记的目标数据进行语义传递，实现了跨域语义对齐，从而实现了分类域对齐。在三个标准基准数据集上进行了广泛的实验，结果表明该方法优于先前最先进的 SSDA 方法，证明了该 G-ABC 算法的优越性。

自适应介数聚类用于半监督域适应

Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation

Unsupervised domain adaptation aims to learn a task classifier that performs
well on the unlabeled target domain, by utilizing the labeled source domain.
Inspiring results have been acquired by learning domain-invariant deep features
via domain-adversarial training. However, its parallel design of task and
domain classifiers limits the ability to achieve a finer category-level domain
alignment. To promote categorical domain adaptation (CatDA), based on a joint
category-domain classifier, we propose novel losses of adversarial training at
both domain and category levels. Since the joint classifier can be regarded as
a concatenation of individual task classifiers respectively for the two
domains, our design principle is to enforce consistency of category predictions
between the two task classifiers. Moreover, we propose a concept of vicinal
domains whose instances are produced by a convex combination of pairs of
instances respectively from the two domains. Intuitively, alignment of the
possibly infinite number of vicinal domains enhances that of original domains.
We propose novel adversarial losses for vicinal domain adaptation (VicDA) based
on CatDA, leading to Vicinal and Categorical Domain Adaptation (ViCatDA). We
also propose Target Discriminative Structure Recovery (TDSR) to recover the
intrinsic target discrimination damaged by adversarial feature alignment. We
also analyze the principles underlying the ability of our key designs to align
the joint distributions. Extensive experiments on several benchmark datasets
demonstrate that we achieve the new state of the art.

提出一种促进分类域联合自适应 (CatDA) 的方法，即利用联合分类器生成对齐一致的分类预测来实现更细粒度的领域对齐，进一步将此方法用于虚拟相邻领域自适应 (VicDA) 中，以及利用目标区分性结构恢复方法来提高领域能力。该方法在多个基准数据集上实现了新的领域自适应最新技术。