聚类分析
利用
超图
计算机科学
关系(数据库)
领域(数学分析)
人工智能
背景(考古学)
源代码
数据挖掘
域适应
过程(计算)
特征(语言学)
编码(集合论)
机器学习
模式识别(心理学)
适应(眼睛)
无监督学习
理论计算机科学
构造(python库)
数据建模
上下文模型
特征学习
特征提取
分割
语义学(计算机科学)
关系抽取
领域知识
作者
Jinkun Jiang,Qingxuan Lv,Yuezun Li,Yong Du,Junyu Dong,Sheng Chen,Hui Yu
标识
DOI:10.1109/tip.2025.3631461
摘要
Source-Free unsupervised Domain Adaptation (SFDA) aims to classify target samples by only accessing a pre-trained source model and unlabelled target samples. Since no source data is available, transferring the knowledge from the source domain to the target domain is challenging. Existing methods normally exploit the pair-wise relation among target samples and attempt to discover their correlations by clustering these samples based on semantic features. The drawbacks of these methods include: 1) the pair-wise relation is limited to exposing the underlying correlations of two more samples, hindering the exploration of the structural information embedded in the target domain; and 2) the clustering process only relies on the semantic feature, while overlooking the critical effect of domain shift, i.e., the distribution differences between the source and target domains. To address these issues, we propose a new SFDA method that exploits the high-order neighborhood relation and explicitly takes the domain shift effect into account. Specifically, we formulate the SFDA as a hypergraph learning problem and construct hyperedges to explore the deep structural and context information among multiple samples. Moreover, we integrate a self-loop strategy into the constructed hypergraph to elegantly introduce the domain uncertainty of each sample. By clustering these samples based on hyperedges, both the semantic feature and domain shift effects are considered. We then describe an adaptive relation-based objective to tune the model with soft attention levels for all samples. Extensive experiments are conducted on Office-31, Office-Home, VisDA, DomainNet-126 and PointDA-10 datasets. The results demonstrate the superiority of our method over state-of-the-art counterparts. Our code is avaliable at https://github.com/OUC-POVA/HG-SFDA.
科研通智能强力驱动
Strongly Powered by AbleSci AI