计算机工程与应用2024，Vol.60Issue(13)：66-80,15.DOI:10.3778/j.issn.1002-8331.2310-0290
Transformer在域适应中的应用研究综述
Review of Research on Application of Transformer in Domain Adaptation
陈健威 1俞璐 1韩昌芝 1李林1
作者信息
- 1. 中国人民解放军陆军工程大学 通信工程学院,南京 210000
- 折叠
摘要
Abstract
Domain adaptation,the important branch of transfer learning,aims to solve the problem that the performance of traditional machine learning algorithms drops sharply when the training and test samples obey different data distribu-tions.Transformer is a deep learning framework based on a self-attention mechanism,which has strong global feature extraction ability and modeling ability.In recent years,the combination of Transformer and domain adaptation has also become a research hotspot.Although many relative methods have been published,the review of Transformer application in domain adaptation has not been reported.In order to fill the gap in this field and provide reference for relevant research,this paper summarizes and analyzes some typical domain adaptation methods based on Transformer in recent years.This paper summarizes the concepts related to domain adaptation and the basic structure of the Transformer,sorts out various domain adaptation methods based on Transformer from four applications,i.e.image classification,image semantic seg-mentation,object detection and medical image analysis and compares the domain adaptation methods in image classifica-tion.Finally,the challenges of the current domain adaptation Transformer model are summarized,and the feasible research directions in the future are discussed.关键词
域适应/迁移学习/Transformer/自注意力机制/深度学习Key words
domain adaptation/transfer learning/Transformer/self-attention mechanism/deep learning分类
信息技术与安全科学引用本文复制引用
陈健威,俞璐,韩昌芝,李林..Transformer在域适应中的应用研究综述[J].计算机工程与应用,2024,60(13):66-80,15.
