深度半监督学习中伪标签方法综述

doi:10.3778/j.issn.1673-9418.2111144

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (6): 1279-1290.DOI: 10.3778/j.issn.1673-9418.2111144

深度半监督学习中伪标签方法综述

刘雅芬¹^,², 郑艺峰¹^,²^,⁺(), 江铃燚¹^,², 李国和³, 张文杰¹^,²

1. 闽南师范大学计算机学院,福建漳州 363000
2. 数据科学与智能应用福建省高校重点实验室,福建漳州 363000
3. 中国石油大学（北京）信息科学与工程学院,北京 102249

收稿日期:2021-11-02 修回日期:2022-01-05 出版日期:2022-06-01 发布日期:2022-01-17
通讯作者: + E-mail: zyf@mnnu.edu.cn
作者简介:刘雅芬（1999—）,女,福建南平人,硕士研究生,CCF会员,主要研究方向为机器学习、深度学习。
郑艺峰（1980—）,男,福建漳州人,博士,讲师,硕士生导师,CCF会员,CAAI会员,主要研究方向为人工智能、机器学习、深度学习。
江铃燚（1998—）,女,福建福州人,硕士研究生,CCF会员,主要研究方向为机器学习、深度学习。
李国和（1965—）,男,福建平和人,博士,教授,博士生导师,主要研究方向为人工智能、机器学习、知识发现。
张文杰（1984—）,男,福建漳州人,博士,教授,硕士生导师,主要研究方向为移动边缘计算、物联网。
基金资助:
国家自然科学基金(62141602);福建省自然科学基金(2021J011002);福建省自然科学基金(2021J011004);克拉玛依科技发展计划项目(2020CGZH0009)

Survey on Pseudo-Labeling Methods in Deep Semi-supervised Learning

LIU Yafen¹^,², ZHENG Yifeng¹^,²^,⁺(), JIANG Lingyi¹^,², LI Guohe³, ZHANG Wenjie¹^,²

1. College of Computer Science, Minnan Normal University, Zhangzhou, Fujian 363000, China
2. Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou, Fujian 363000, China
3. College of Information Science and Engineering, China University of Petroleum, Beijing 102249, China

Received:2021-11-02 Revised:2022-01-05 Online:2022-06-01 Published:2022-01-17
About author:LIU Yafen, born in 1999, M.S. candidate, member of CCF. Her research interests include machine learning and deep learning.
ZHENG Yifeng, born in 1980, Ph.D., lecturer, M.S. supervisor, member of CCF and CAAI. His research interests include artificial intelligence, machine learning and deep learning.
JIANG Lingyi, born in 1998, M.S. candidate, member of CCF. Her research interests include machine learning and deep learning.
LI Guohe, born in 1965, Ph.D., professor, Ph.D. supervisor. His research interests include artificial intelligence, machine learning and knowledge discovery.
ZHANG Wenjie, born in 1984, Ph.D., professor, M.S. supervisor. His research interests include mobile edge computing and Internet of things.
Supported by:
National Natural Science Foundation of China(62141602);Natural Science Foundation of Fujian Province(2021J011002);Natural Science Foundation of Fujian Province(2021J011004);Kalamay Science & Technology Research Project(2020CGZH0009)

摘要/Abstract

摘要：

随着智能技术的发展,深度学习已成为机器学习的研究热点,在各个领域发挥着越来越重要的作用。深度学习需要大量的标签数据用于提升模型性能。为了有效解决标签问题,研究人员将半监督学习与深度学习相结合。同时利用少量的标签数据和大量的无标签数据构建模型,有利于扩大样本空间。鉴于深度半监督学习的理论意义和实际应用价值,以深度半监督学习方法中的伪标签方法作为切入点进行分析。首先,对深度半监督学习进行介绍,指出伪标签方法优势所在;其次,从自训练和多视角训练角度出发对伪标签方法进行阐述,对已有的模型进行综合性分析;接着,重点介绍基于图和伪标签的标签传播方法,并对已有伪标签方法进行实验分析;最后,从无标签数据效用性、噪声数据、合理性和伪标签方法的结合上总结伪标签方法所面临的问题和未来研究方向。

关键词: 深度学习, 半监督学习, 伪标签, 标签传播

Abstract:

With the development of intelligent technology, deep learning has become a hot topic in machine learning. It is playing a more and more important role in various fields. Deep learning requires a lot of labeled data to imp-rove model performance. Therefore, researchers effectively combine semi-supervised learning with deep learning to solve the labeled data problem. It utilizes a small amount of labeled data and a large amount of unlabeled data to build the model simultaneously. It can help to expand the sample space. In view of its theoretical significance and practical application value, this paper focuses on the pseudo-labeling methods as the starting point. Firstly, deep semi-supervised learning is introduced and the advantage of pseudo-labeling methods is pointed out. Secondly, the pseudo-labeling methods are described from self-training and multi-view training and the existing model is comprehensively analyzed. And then, the label propagation method based on graph and pseudo-labeling is introduced. Furthermore, the existing pseudo-labeling methods are analyzed and compared. Finally, the problems and future research direction of pseudo-labeling methods are summarized from the utility of unlabeled data, noise data, rationality, and the combi-nation of pseudo-labeling methods.

Key words: deep learning, semi-supervised learning, pseudo-labeling, label propagation

中图分类号:

刘雅芬, 郑艺峰, 江铃燚, 李国和, 张文杰. 深度半监督学习中伪标签方法综述[J]. 计算机科学与探索, 2022, 16(6): 1279-1290.

LIU Yafen, ZHENG Yifeng, JIANG Lingyi, LI Guohe, ZHANG Wenjie. Survey on Pseudo-Labeling Methods in Deep Semi-supervised Learning[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(6): 1279-1290.

图/表 11

图1 监督学习、半监督学习、无监督学习结构对比

Fig.1 Structure comparison of supervised learning, semi-supervised learning and unsupervised learning

图2 无标签数据效用示例（黑点为无标签数据）

Fig.2 Unlabeled data utility example (black dots indicate unlabeled data)

图3 代理标签模型

Fig.3 Proxy-label model

图4 噪声学生模型

Fig.4 Noisy student model

图5 自半监督学习模型

Fig.5 Self-supervised semi-supervised learning model

图6 元伪标签模型

Fig.6 Meta pseudo labels model

图7 协同训练模型

Fig.7 Co-training model

图8 三体训练模型

Fig.8 Tri-training model

表1 实验中使用的UCI数据集

Table 1 UCI datasets used in experiment

数据集	节点（样本）	特征	类别	类别分布
Iris	150	4	3	50,50,50
Cmc	1 473	9	3	629,333,511
Iono	351	34	2	225,126

表2 伪标签方法在不同图像数据集上实验结果

Table 2 Experimental results of pseudo-labeling method on different image datasets %

方法	CIFAR-10	CIFAR-100	ILSVRC-2012
熵最小化	86.41	—	83.39
代理标签	—	—	82.41
噪声学生	—	—	88.39
元伪标签	88.62	—	90.20
自半监督	—	—	91.23
协同训练	90.97	65.37	—
三体训练	91.55	70.26	—

表3 伪标签方法在不同UCI数据集上实验结果

Table 3 Experimental results of pseudo-labeling method on different UCI datasets %

方法	Iris	Cmc	Ionosphere
协同训练	75.21	32.33	63.82
三体训练	80.03	35.92	64.13
标签传播	85.02	40.95	67.65

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深度半监督学习中伪标签方法综述

Survey on Pseudo-Labeling Methods in Deep Semi-supervised Learning

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