广东工业大学学报2024，Vol.41Issue(6)：80-90,11.DOI:10.12052/gdutxb.230172
一种基于邻居环境感知的主动领域自适应算法
Active Domain Adaptation Based on Neighbor Environment Perception Sample Selection
摘要
Abstract
Active domain adaptation(ADA)aims to train an effective model under the context of domain adaptation with as few queried instances as possible.However,existing algorithms tend to select instances that are either uninformative,redundant,or outliers due to domain shift.To address this issue,a novel approach called neighbor environment perception sample selection(NEPS)for active domain adaptation is proposed.NEPS explores the target sample informativeness in a neighbor environment-aware manner to select instances that are potentially most valuable under domain shift.Specifically,from informativeness perspective,NEPS aims to acquire knowledge not only from individual data points but also from their neighboring samples.This is achieved by measuring neighbor awareness informativeness score(NAIS),which ensures the selected samples have both high individual informativeness score and environment informativeness score.Additionally,NEPS ranks and selects samples based on their similarity scores with labeled samples to ensure diversity among the chosen instances.Furthermore,NEPS makes effective use of all labeled samples as well as a large amount of unlabeled data from the target domain to enhance the model's performance.Experimental results demonstrate that NEPS exhibits strong sample selection capability and outperforms existing models in terms of classification performance on various benchmark datasets.关键词
主动领域自适应/信息性/多样性/邻居环境感知Key words
active domain adaptation/informativeness/diversity/neighbor environment perception分类
信息技术与安全科学引用本文复制引用
陈鑫瑀,朱鉴,陈炳丰,蔡瑞初..一种基于邻居环境感知的主动领域自适应算法[J].广东工业大学学报,2024,41(6):80-90,11.基金项目
国家自然科学基金资助重点项目(62237001) （62237001）
国家重点研发计划项目(2021ZD0111501) （2021ZD0111501）
国家自然科学基金优秀青年基金资助项目(6212200101) （6212200101）
国家自然科学基金资助面上项目(62272298,62176066,61976052) （62272298,62176066,61976052）
广州市科技计划项目(202002030110,202007040005) （202002030110,202007040005）
