A Human-In-One-Loop Active Domain Adaptation Framework for Digit Recognition

IF 2.9 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Artificial Intelligence Pub Date : 2024-05-11 DOI:10.1080/08839514.2024.2349410
Hao Xiu, Guanchen Li, Jie He, Xiaotong Zhang, Yue Qi
{"title":"A Human-In-One-Loop Active Domain Adaptation Framework for Digit Recognition","authors":"Hao Xiu, Guanchen Li, Jie He, Xiaotong Zhang, Yue Qi","doi":"10.1080/08839514.2024.2349410","DOIUrl":null,"url":null,"abstract":"Domain adaptation can effectively enhance a model’s performance on target domain data with limited data. However, when some target domain labels are obtainable, training the model with both source ...","PeriodicalId":8260,"journal":{"name":"Applied Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/08839514.2024.2349410","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Domain adaptation can effectively enhance a model’s performance on target domain data with limited data. However, when some target domain labels are obtainable, training the model with both source ...
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
用于数字识别的人一环主动域适应框架
域适应可以在数据有限的情况下有效提高模型在目标域数据上的性能。然而,当可以获得一些目标域标签时,同时使用源数据和目标域数据训练模型的效果并不理想。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Applied Artificial Intelligence
Applied Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
5.20
自引率
3.60%
发文量
106
审稿时长
6 months
期刊介绍: Applied Artificial Intelligence addresses concerns in applied research and applications of artificial intelligence (AI). The journal also acts as a medium for exchanging ideas and thoughts about impacts of AI research. Articles highlight advances in uses of AI systems for solving tasks in management, industry, engineering, administration, and education; evaluations of existing AI systems and tools, emphasizing comparative studies and user experiences; and the economic, social, and cultural impacts of AI. Papers on key applications, highlighting methods, time schedules, person-months needed, and other relevant material are welcome.
期刊最新文献
Coupled Spatial-Spectral Constrained Convolutional Fusion Network for Hyperspectral and Panchromatic images Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data A Red Teaming Framework for Securing AI in Maritime Autonomous Systems Machine Learning Ensemble Classifiers for Feature Selection in Rice Cultivars Fraud Detection Based on Credit Review Texts with Dual Channel Memory Networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1