基于特征异常检测与伪标签回归的无监督对抗域适应

潘杰; 刘波; 邹筱瑜

doi:10.12263/DZXB.20240074

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基于特征异常检测与伪标签回归的无监督对抗域适应

学术论文 | 更新时间：2025-12-08

- 基于特征异常检测与伪标签回归的无监督对抗域适应
- Feature Anomaly Detection and Pseudo-Label Regression for Adversarial Domain Adaptation
- 电子学报 2025年53卷第1期页码：128-140
- 作者机构：
  
  1.中国矿业大学信息与控制工程学院，江苏徐州 221116
  2.中国矿业大学机电工程学院，江苏徐州 221116
- 作者简介：
  
  潘杰男，1986年12月出生于江苏省徐州市.现为中国矿业大学信息与控制工程学院副教授、硕士生导师.主要研究方向为机器学习与网络生理学.
  刘波男，1997年10月出生于湖南省邵阳市.2023年毕业于中国矿业大学信息与控制工程学院.现为中国电信股份有限公司长沙分公司工程师.从事算法设计与云网设施维护工作.E-mail: liubo7556@163.com
  邹筱瑜女，1990年5月出生于四川省自贡市.现为中国矿业大学机电工程学院副教授、硕士生导师.主要研究方向为机器学习与装备运维.
- 基金信息：
  
  国家自然科学基金(62176258;62273349;61806207);中央高校基本科研业务费专项资金项目(2021YCPY0111)
- DOI：10.12263/DZXB.20240074
  中图分类号： TP181
- 收稿：2024-01-15，
  
  修回：2024-09-06，
  
  纸质出版：2025-01-25
- 稿件说明：
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潘杰, 刘波, 邹筱瑜. 基于特征异常检测与伪标签回归的无监督对抗域适应[J]. 电子学报, 2025, 53(01): 128-140.

PAN Jie, LIU Bo, ZOU Xiao-yu. Feature Anomaly Detection and Pseudo-Label Regression for Adversarial Domain Adaptation[J]. Acta Electronica Sinica, 2025, 53(01): 128-140.
潘杰, 刘波, 邹筱瑜. 基于特征异常检测与伪标签回归的无监督对抗域适应[J]. 电子学报, 2025, 53(01): 128-140. DOI：10.12263/DZXB.20240074

PAN Jie, LIU Bo, ZOU Xiao-yu. Feature Anomaly Detection and Pseudo-Label Regression for Adversarial Domain Adaptation[J]. Acta Electronica Sinica, 2025, 53(01): 128-140. DOI：10.12263/DZXB.20240074

摘要

无监督域适应任务中源域和目标域通常不满足独立同分布假设. 为生成目标域可用标签，经典域适应方法选择分类器预测概率最大的类别作为目标样本伪标签，使伪标签中可能包含噪声信息，造成域适应“负迁移”. 此外，传统对抗域适应方法往往考虑对齐领域间全局分布，较少关注样本类别信息，如何在域适应任务中提取判别性类别级特征至关重要. 为此，本文提出一种基于特征异常检测与伪标签回归的无监督对抗域适应方法. 通过分类器预测同类别目标样本组成目标域类别子域，引入高斯均匀混合模型检测与类均值特征距离异常的子域样本，计算样本后验概率并以此度量子域中样本伪标签的正确性，作为损失因子限制伪标签在训练中对模型的影响. 同时，采用伪标签回归函数减小分类器预测标签与高置信度伪标签差异，对无标签目标域进行类别约束，提高特征类别可辨别性. 实验表明，所提方法在数据集Office-31、Image-CLEF和Office-Home上平均识别精度分别为90.2%、89.6%和69.5%，较相关主流算法均有提升.

Abstract

In unsupervised domain adaptation tasks

the source and target domains usually do not satisfy the independent and identical distribution assumption. In order to generate the usable labels for the target domain

classical domain adaptation methods select the category with the highest prediction probability of the classifier as the pseudo-label of the target sample. Thus

the pseudo-label inevitably contains certain noise information

which may cause negative transfer to the domain adaptation model. In addition

traditional adversarial domain adaptation methods usually consider the global distribution between domains and ignore the category information of samples. How to extract discriminative category-level features in domain adaptation tasks is also an important problem. Therefore

an unsupervised adversarial domain adaptation method is proposed using feature anomaly detection and pseudo-label regression. The target samples of the same class predicted by the classifier are formed into the category subdomain within the target domain. The Gaussian uniform mixture model is used to detect the subdomain samples with abnormal distance from the class mean. The posterior probability of the samples is calculated and the correctness of the sample pseudo-labels in the subdomain is measured

which is used as a loss factor to limit the influence of pseudo-labels on the model in training. Meanwhile

the pseudo-label regression function is used to reduce the difference between the predicted label and the high-confidence pseudo-label of the classifier. The category constraint of the unlabeled target domain is adopted to improve the distinguishability of feature categories. Experimental results show that the average recognition accuracy of the proposed method on datasets Office-31

Image-CLEF

and Office-Home are 90.2%

89.6%

and 69.5%

respectively

which are all higher than the related popular algorithms.

关键词

Keywords

references

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