.png){kind=icon}
返回
Self-training: A survey
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2024.128904.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Self-training methods have gained significant attention in recent years due to their effectiveness in leveraging small labeled datasets and large unlabeled observations for prediction tasks. These models identify decision boundaries in low-density regions without additional assumptions about data distribution, using the confidence scores of a learned classifier. The core principle of self-training involves iteratively assigning pseudo-labels to unlabeled samples with confidence scores above a certain threshold, enriching the labeled dataset and retraining the classifier. This paper presents self-training methods for binary and multi-class classification, along with variants and related approaches such as consistency-based methods and transductive learning. We also briefly describe self-supervised learning and reinforced self-training. Furthermore, we highlight popular applications of self-training and discuss the importance of dynamic thresholding and reducing pseudo-label noise for performance improvement. To the best of our knowledge, this is the first thorough and complete survey on self-training.
Keyword:
Semi-supervised learning
Self-training
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
miRNA-Based Potential Biomarkers and New Molecular Insights in Ulcerative Colitis溃疡性结肠炎中基于miRNA的潜在生物标志物和新分子见解
OAAI
OAAI分享收藏分享收藏分享收藏A Comprehensive Survey of Image Augmentation Techniques for Deep Learning.png)
PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏
查看更多内容