Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation
- URL: http://arxiv.org/abs/2401.11448v1
- Date: Sun, 21 Jan 2024 09:57:56 GMT
- Title: Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation
- Authors: Jichang Li, Guanbin Li, Yizhou Yu
- Abstract summary: We propose a novel SSDA approach named Graph-based Adaptive Betweenness Clustering (G-ABC) for achieving categorical domain alignment.
Our method outperforms previous state-of-the-art SSDA approaches, demonstrating the superiority of the proposed G-ABC algorithm.
- Score: 108.40945109477886
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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