基于跨空间多尺度信息聚合和推理一致性的域泛化方法

黎拓新; 项凤涛; 陈君海; 张晓博; 吕云霄

doi:10.11959/j.issn.2096-6652.202516

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基于跨空间多尺度信息聚合和推理一致性的域泛化方法

学术论文 | 更新时间：2025-07-21

- 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
- 智能科学与技术学报 2025年7卷第2期页码：200-210
- 作者机构：
  
  国防科技大学智能科学学院，湖南长沙 410073
- 作者简介：
  
  [ "黎拓新（2002- ），男，国防科技大学智能科学学院硕士生，主要研究方向为模式识别、人工智能、迁移学习。" ]
  [ "项凤涛（1986- ），男，博士，国防科技大学智能科学学院副教授，主要研究方向为智能辅助决策、不确定性推理、智能控制。" ]
  [ "陈君海（2001- ），男，国防科技大学智能科学学院硕士生，主要研究方向为人工智能、谣言检测。" ]
  [ "张晓博（2002- ），男，国防科技大学智能科学学院硕士生，主要研究方向为人工智能、增量学习。" ]
  [ "吕云霄（1985- ），女，国防科技大学智能科学学院高级工程师、副处长，主要研究方向为导航技术。" ]
- 基金信息：
  
  国家自然科学基金项目(62473371)
- DOI：10.11959/j.issn.2096-6652.202516
  中图分类号： TP391.4
- 收稿：2025-01-21，
  
  修回：2025-04-11，
  
  纸质出版：2025-06-15
- 稿件说明：
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黎拓新,项凤涛,陈君海等.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,2025,07(02):200-210.

LI Tuoxin,XIANG Fengtao,CHEN Junhai,et al.Domain generalization method based on cross-space multi-scale information aggregation and inference consistency[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):200-210.
黎拓新,项凤涛,陈君海等.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,2025,07(02):200-210. DOI： 10.11959/j.issn.2096-6652.202516.

LI Tuoxin,XIANG Fengtao,CHEN Junhai,et al.Domain generalization method based on cross-space multi-scale information aggregation and inference consistency[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):200-210. DOI： 10.11959/j.issn.2096-6652.202516.

摘要

在机器学习中，通常模型假设训练数据和测试数据来自相同的分布，然而在实际应用中，数据分布往往存在差异，导致域漂移问题，进而影响模型的泛化能力。现有域泛化方法主要集中在提取域不变特征，忽视了域特定特征对模型预测的影响。为此，提出了一种基于跨空间多尺度信息聚合的域鉴别器，通过多尺度信息捕捉，有效去除域特定特征，增强域不变特征的提取能力；同时通过动量更新推理一致性损失函数，利用样本类别中心的推理一致性，进一步提高模型的鲁棒性。在多个公开数据集上进行了对比实验与分析，结果表明，新方法在域泛化中具有更好的性能表现，有效缓解了域特定特征对模型性能的影响，可为处理域漂移问题提供技术参考。

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 features 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 domain 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

Keywords

references

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