智能科学与技术学报2025，Vol.7Issue(2)：200-210,11.DOI:10.11959/j.issn.2096-6652.202516
基于跨空间多尺度信息聚合和推理一致性的域泛化方法
Domain generalization method based on cross-space multi-scale information aggregation and inference consistency
摘要
Abstract
In machine learning,it typically assumes that training data and testing data of models are drawn from the same distribution.However,in real-world applications,data distributions often differ,resulting in domain shift problems that adversely affect model generalization.Existing domain generalization methods primarily focus on extracting domain-invariant features while overlooking the potential impact of domain-specific features on model predictions.To address this issue,a domain discriminator based on cross-space multi-scale information aggregation was proposed.By capturing multi-scale information,domain-specific features were effectively removed and the extraction of domain-invariant fea-tures was enhanced.Additionally,the momentum update inference consistency loss function was employed to leverage the inference consistency of sample category centers,further improving model robustness.Comparative experiments and analysis conducted on multiple public datasets demonstrate that the proposed method exhibits superior performance in do-main generalization,effectively mitigating the impact of domain-specific features on model performance and providing a technical reference for addressing domain shift problems.关键词
域泛化/迁移学习/域漂移/注意力机制/推理一致性Key words
domain generalization/transfer learning/domain drift/attention mechanism/inference consistency分类
信息技术与安全科学引用本文复制引用
黎拓新,项凤涛,陈君海,张晓博,吕云霄..基于跨空间多尺度信息聚合和推理一致性的域泛化方法[J].智能科学与技术学报,2025,7(2):200-210,11.基金项目
国家自然科学基金项目(No.62473371) The National Natural Science Foundation of China(No.62473371) （No.62473371）
