[1]YANG Liran,ZHANG Xiaolong,ZHAO Zhenbing,et al.Prototype guidance and dual-level contrastive learning for source-free domain adaptation[J].CAAI transactions on intelligent systems,2026,21(5):1130-1141.[doi:10.11992/tis.202508033]
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Prototype guidance and dual-level contrastive learning for source-free domain adaptation

CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 5 Page number: 1130-1141 Column: 学术论文—机器学习 Public date: 2026-09-05
Title:
Prototype guidance and dual-level contrastive learning for source-free domain adaptation
Author(s):
YANG Liran1; 2; 3;  ZHANG Xiaolong2;  ZHAO Zhenbing1; 4;  ZHAI Yongjie1; 2;  SU Pan2; 5
1. Yanzhao Electric Power Laboratory, North China Electric Power University, Baoding 071003, China;
2. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
3. Engineering Research Center of Intelligent Computing for Complex Energy Systems of Ministry of Education, North China Electric Power University, Baoding 071003, China;
4. School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China;
5. Hebei Key Laboratory of Knowledge Computing for Energy and Power, North China Electric Power University, Baoding 071003, China
Keywords:
source-free domain adaptation; prototype guidance; contrastive learning; pseudo-label; feature fusion; prototype orthogonality; self-training; feature alignment
CLC:
TP391
DOI:
10.11992/tis.202508033
Abstract:
Source-free domain adaptation (SFDA) aims to enable effective knowledge transfer of pre-trained models from the source domain to the target domain when the source samples are inaccessible. Most SFDA methods adopt self-training learning, but they are susceptible to noisy pseudo-labels, making it difficult for models to accurately capture intrinsic features and leading to performance degradation. To address this issue, we propose a novel SFDA method named prototype guidance and dual-level contrastive learning (PDCL). Specifically, PDCL first extracts features from the source domain using the pre-trained source model to construct initial class-level prototype representations for the target domain. Subsequently, it aligns the features of source and target samples via a feature fusion strategy, thereby generating pseudo-labels for the target domain. Secondly, it introduces a prototype orthogonality loss to enhance discriminability by maximizing the orthogonality between class prototypes. Finally, PDCL employs a dual-level contrastive learning strategy, where instance-level contrast is conducted in the decision space to pull near neighbors closer and push outliers away, while prototype-level contrast is conducted in the feature space to enhance structural consistency. Experimental results on Office-31, Office-Home, and DomainNet-126 datasets show that PDCL outperforms state-of-the-art methods, which validates its effectiveness.
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