Semi-supervised learning(SSL)aims to improve performance by exploiting unlabeled data when labels are scarce.Conventional SSL studies typically assume close environments where important factors(e.g.,label,feature,dist...Semi-supervised learning(SSL)aims to improve performance by exploiting unlabeled data when labels are scarce.Conventional SSL studies typically assume close environments where important factors(e.g.,label,feature,distribution)between labeled and unlabeled data are consistent.However,more practical tasks involve open environments where important factors between labeled and unlabeled data are inconsistent.It has been reported that exploiting inconsistent unlabeled data causes severe performance degradation,even worse than the simple supervised learning baseline.Manually verifying the quality of unlabeled data is not desirable,therefore,it is important to study robust SSL with inconsistent unlabeled data in open environments.This paper briefly introduces some advances in this line of research,focusing on techniques concerning label,feature,and data distribution inconsistency in SSL,and presents the evaluation benchmarks.Open research problems are also discussed for reference purposes.展开更多
基金supported by the Key Program of Jiangsu Science Foundation(BK20243012)the National Natural Science Foundation of China(NSFC)(Grant Nos.62306133,62176118).
文摘Semi-supervised learning(SSL)aims to improve performance by exploiting unlabeled data when labels are scarce.Conventional SSL studies typically assume close environments where important factors(e.g.,label,feature,distribution)between labeled and unlabeled data are consistent.However,more practical tasks involve open environments where important factors between labeled and unlabeled data are inconsistent.It has been reported that exploiting inconsistent unlabeled data causes severe performance degradation,even worse than the simple supervised learning baseline.Manually verifying the quality of unlabeled data is not desirable,therefore,it is important to study robust SSL with inconsistent unlabeled data in open environments.This paper briefly introduces some advances in this line of research,focusing on techniques concerning label,feature,and data distribution inconsistency in SSL,and presents the evaluation benchmarks.Open research problems are also discussed for reference purposes.
