基于特征校正的多对抗域适应方法

张永; 刘昊双; 章琪; 刘文哲

doi:10.11959/j.issn.1000-0801.2024014

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基于特征校正的多对抗域适应方法

研究与开发 | 更新时间：2024-06-05

- 基于特征校正的多对抗域适应方法
- Multi-adversarial domain adaptation method based on feature correction
- 电信科学 2024年40卷第1期页码：71-82
- 作者机构：
  
  1. 湖州师范学院信息工程学院，浙江湖州 313000
  2. 辽宁师范大学计算机与信息技术学院，辽宁大连 116081
- 作者简介：
  
  [ "张永（1975- ），男，博士，湖州师范学院教授、博士生导师，辽宁师范大学计算机与信息技术学院教授，主要研究方向为数据挖掘、智能计算、情感计算" ]
  [ "刘昊双（1996- ），女，辽宁师范大学计算机与信息技术学院硕士生，主要研究方向为数据挖掘、机器学习" ]
  [ "章琪（1999- ），女，辽宁师范大学计算机与信息技术学院硕士生，主要研究方向为机器学习、智能计算" ]
  [ "刘文哲（1983- ），女，博士，湖州师范学院讲师，主要研究方向为多视角学习、深度学习" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61772252);辽宁省教育厅科学研究经费资助项目;The Scientific Research Foundation of the Education Department of Liaoning Province(LJKZ0965);湖州市科技计划项目;The Huzhou Science and Technology Plan Project(2022GZ08);湖州市科技计划项目;The Huzhou Science and Technology Plan Project(2023ZD2004)
- DOI：10.11959/j.issn.1000-0801.2024014
  中图分类号： TP181
- 网络出版日期：2024-01，
  
  纸质出版日期：2024-01-20
- 稿件说明：
移动端阅览
张永, 刘昊双, 章琪, 等. 基于特征校正的多对抗域适应方法[J]. 电信科学, 2024,40(1):71-82.

Yong ZHANG, Haoshuang LIU, Qi ZHANG, et al. Multi-adversarial domain adaptation method based on feature correction[J]. Telecommunications science, 2024, 40(1): 71-82.
张永, 刘昊双, 章琪, 等. 基于特征校正的多对抗域适应方法[J]. 电信科学, 2024,40(1):71-82. DOI： 10.11959/j.issn.1000-0801.2024014.

Yong ZHANG, Haoshuang LIU, Qi ZHANG, et al. Multi-adversarial domain adaptation method based on feature correction[J]. Telecommunications science, 2024, 40(1): 71-82. DOI： 10.11959/j.issn.1000-0801.2024014.

摘要

领域自适应可以通过对齐源域和目标域的分布将有标签的源域信息迁移到没有标签但相关的目标域。然而，现有的大多数方法仅对源域和目标域的低层特征分布进行对齐，无法捕获样本中的细粒度信息。基于此，提出了一种基于特征校正的多对抗域适应方法。该方法在引入注意力机制以突出可迁移区域的基础上，通过部署特征校正模块对齐两个域之间的高级特征分布，进一步缩小域差异。此外，为了避免单个分类器过度拟合其自身的噪声伪标签，还提出了双分类器协同训练，并利用图神经网络特征聚合的特性生成更精准的源域标签。在3个迁移学习基准数据集上的大量实验证明所提方法的有效性。

Abstract

Domain adaptation can transfer labeled source domain information to an unlabeled but related target domain by aligning the distribution of source domain and target domain.However

most existing methods only align the low-level feature distributions of the source and target domains

failing to capture fine-grained information within the samples.To address this limitation

a feature correction-based multi-adversarial domain adaptation method was proposed.An attention mechanism to highlight transferable regions was introduced in this method and a feature correction module was deployed to align the high-level feature distributions between the two domains

further reducing domain discrepancies.Additionally

to prevent individual classifiers from overfitting their own noisy pseudo-labels

dual classifier co-training was proposed and the feature aggregation property of graph neural networks was utilized to generate more accurate source domain labels.Extensive experiments on three benchmark datasets for transfer learning demonstrate the effectiveness of the proposed method.

关键词

Keywords

references

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