ComCo: Complementary supervised contrastive learning for complementary label learning

doi:10.1016/j.neunet.2023.10.013

	ComCo: Complementary supervised contrastive learning for complementary label learning
	Jiang, Haoran 1,5,6; Sun, Zhihao 2,3; Tian, Yingjie 4,5,6,7
	2024
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
卷号	169 页码:44-56
摘要	Complementary label learning (CLL) is an important problem that aims to reduce the cost of obtaining largescale accurate datasets by only allowing each training sample to be equipped with labels the sample does not belong. Despite its promise, CLL remains a challenging task. Previous methods have proposed new loss functions or introduced deep learning-based models to CLL, but they mostly overlook the semantic information that may be implicit in the complementary labels. In this work, we propose a novel method, ComCo, which leverages a contrastive learning framework to assist CLL. Our method includes two key strategies: a positive selection strategy that identifies reliable positive samples and a negative selection strategy that skillfully integrates and leverages the information in the complementary labels to construct a negative set. These strategies bring ComCo closer to supervised contrastive learning. Empirically, ComCo significantly achieves better representation learning and outperforms the baseline models and the current state-of-the-art by up to 14.61% in CLL.
关键词	Complementary label learning Weakly supervised learning Contrastive learning Machine learning Representation learning
DOI	10.1016/j.neunet.2023.10.013
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[12071458,71731009]
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:001103885400001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38062
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Tian, Yingjie
作者单位	1.Univ Chinese Acad Sci, Sch Math & Sci, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 4.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China 7.UCAS, MOE Social Sci Lab Digital Econ Forecasts & Policy, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Jiang, Haoran,Sun, Zhihao,Tian, Yingjie. ComCo: Complementary supervised contrastive learning for complementary label learning[J]. NEURAL NETWORKS,2024,169:44-56.
APA	Jiang, Haoran,Sun, Zhihao,&Tian, Yingjie.(2024).ComCo: Complementary supervised contrastive learning for complementary label learning.NEURAL NETWORKS,169,44-56.
MLA	Jiang, Haoran,et al."ComCo: Complementary supervised contrastive learning for complementary label learning".NEURAL NETWORKS 169(2024):44-56.