Unsupervised Domain Adaptation Based on Joint Hierarchical Granularity Envelope Discriminative Feature Learning

LI Pu-fei; WANG Pin; LI Yong-ming; ZHANG Jin-hua; YAN Fang

doi:10.12263/DZXB.20240793

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Unsupervised Domain Adaptation Based on Joint Hierarchical Granularity Envelope Discriminative Feature Learning

更新时间：2025-03-04

- Unsupervised Domain Adaptation Based on Joint Hierarchical Granularity Envelope Discriminative Feature Learning
- ACTA ELECTRONICA SINICA Pages: 1-13(2025)
- 作者机构：
  
  重庆大学微电子与通信工程学院，重庆 400000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(U21A20448;61771080)
- DOI：10.12263/DZXB.20240793
  CLC： TP391.4;
- Received：28 August 2024，
  
  Revised：2024-11-11，
  
  Published Online：04 March 2025，
- 稿件说明：
移动端阅览
LI Pu-fei, WANG Pin, LI Yong-ming, et al. Unsupervised Domain Adaptation Based on Joint Hierarchical Granularity Envelope Discriminative Feature Learning[J/OL]. ACTA ELECTRONICA SINICA, 2025, 1-13.
DOI：

LI Pu-fei, WANG Pin, LI Yong-ming, et al. Unsupervised Domain Adaptation Based on Joint Hierarchical Granularity Envelope Discriminative Feature Learning[J/OL]. ACTA ELECTRONICA SINICA, 2025, 1-13. DOI： 10.12263/DZXB.20240793.

摘要

无监督领域自适应（Unsupervised Domain Adaptation，UDA）是模式识别中的重要研究领域，旨在将标注完善但分布不同的源域知识转移到未标注的目标域数据上.现有方法仅关注源域和目标域原始样本分布的对齐，因此在分布差异较大时效果不佳.近年来，基于语义的UDA方法引入了类别信息.然而，类别信息过于粗略，难以充分反映源域和目标域的分布.为了解决这一问题，本文提出了一种用于无监督领域自适应的联合层级粒包络判别特征学习方法，该方法在三个层次上聚合了原始样本对、类别和粒包络信息，从而由粗到细地反映了数据分布.具体来说，本文引入了“知识金字塔”理论，通过构造粒包络，连接原始样本与类中心，建立三层粒度样本，替代现有方法中单层粒度的原始样本.同时，基于本文方法设计了一种迭代式聚类算法，揭示了样本间的关联信息，在原始样本与类中心之间生成粒包络.通过使用Bagging集成模式整合这三种粒度层，并对不同层次的粒度进行加权，以确保令人满意的精度.在基准数据集上的实验结果表明，本文方法能有效减小域间差异并提升分类精度，且优于其它主流领域自适应方法.

Abstract

Unsupervised domain adaptation(UDA) is a significant research area that aims to transfer knowledge from source data

which is well-labeled but distributed differently

to unlabeled target data. Existing methods only considered aligning the distributions of the original source and target domain samples

suffering from the big difference in the distributions. In recent years

semantic-based UDA has integrated category information on this basis. However

the category information is too coarse and cannot fully reflect the distributions of source and target domain. To solve this problem

a joint hierarchical granularity envelope(JHGE) discriminative feature learning approach is proposed

which integrates information from original sample pairs

关键词

Keywords

references

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