.png){kind=icon}
Return
Robust semi-supervised learning in open environments
.png){kind=icon}
OA
AI
DOI：10.1007/s11704-024-40646-w.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
machine learning
open environment
semi-supervised learning
robust SSL
Journal
IF:
4.6
Papers:
1.6K
Citations:
2.8K
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveShareSaveDry Separation of Palm Kernel and Palm Shell Using a Novel Five-Stage Winnowing Column System.png)
Technologies
OAAI
TechnologiesIF0
OAAIShareSaveShareSave
View more