通过图神经网络捕捉数据结构信息，提升半监督域适应性能
AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation
- 构建实例图并用图卷积传播结构信息
- 在多个基准上超越现有最优方法
- 适合关注域泛化与结构建模的研究者
在半监督域适应（SSDA）中，模型旨在利用部分标注的目标域数据和大量源域标注数据，提升对目标域的泛化能力。其核心优势在于显著降低对标注数据的依赖，从而减少数据准备的成本与时间。现有大多数方法仅利用域标签和类别标签信息，忽略了数据的结构特性。为此，本文提出一种图学习视角（AGLP）用于半监督域适应。通过在实例图上应用图卷积网络，使结构信息沿加权边传播。所提AGLP模型具有多重优势：首先，据我们所知，这是首个在SSDA中建模结构信息的工作；其次，该模型能有效学习域不变与语义表示，缓解域间差异。在多个标准基准上的大量实验表明，所提出的AGLP算法优于当前最先进的半监督域适应方法。
原文摘要 · Abstract (English)
In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its generalization capability for the target domain. A key advantage of SSDA is its ability to significantly reduce reliance on labeled data, thereby lowering the costs and time associated with data preparation. Most existing SSDA methods utilize information from domain labels and class labels but overlook the structural information of the data. To address this issue, this paper proposes a graph learning perspective (AGLP) for semi-supervised domain adaptation. We apply the graph convolutional network to the instance graph which allows structural information to propagate along the weighted graph edges. The proposed AGLP model has several advantages. First, to the best of our knowledge, this is the first work to model structural information in SSDA. Second, the proposed model can effectively learn domain-invariant and semantic representations, reducing domain discrepancies in SSDA. Extensive experimental results on multiple standard benchmarks demonstrate that the proposed AGLP algorithm outperforms state-of-the-art semi-supervised domain adaptation methods.
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