Domain generalization method based on cross-space multi-scale information aggregation and inference consistency

LI Tuoxin; XIANG Fengtao; CHEN Junhai; ZHANG Xiaobo; LYU Yunxiao

doi:10.11959/j.issn.2096-6652.202516

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Domain generalization method based on cross-space multi-scale information aggregation and inference consistency

Papers and Reports | 更新时间：2025-07-21

- Domain generalization method based on cross-space multi-scale information aggregation and inference consistency
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 2, Pages: 200-210(2025)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(62473371)
- DOI：10.11959/j.issn.2096-6652.202516
  CLC： TP391.4
- Received：21 January 2025，
  
  Revised：2025-04-11，
  
  Published：15 June 2025
- 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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