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Self-supervised image clustering from multiple incomplete views via constrastive complementary generation

文献类型：期刊论文

作者Wang, Jiatai3; Xu, Zhiwei2,3; Yang, Xuewen1; Guo, Dongjin3; Liu, Limin3
刊名IET COMPUTER VISION
出版日期2022-10-10
页码14
关键词clustering from multiple incomplete views computer vision constrastive learning generative adversarial network
ISSN号1751-9632
DOI10.1049/cvi2.12147
英文摘要Incomplete Multi-View Clustering aims to enhance clustering performance by using data from multiple modalities. Despite the fact that several approaches for studying this issue have been proposed, the following drawbacks still persist: (1) It is difficult to learn latent representations that account for complementarity yet consistency without using label information; (2) and thus fails to take full advantage of the hidden information in incomplete data results in suboptimal clustering performance when complete data is scarce. In this study, Contrastive Incomplete Multi-View Image Clustering with Generative Adversarial Networks (CIMIC-GAN), which uses Generative Adversarial Network (GAN) to fill in incomplete data and uses double contrastive learning to learn consistency on complete and incomplete data is proposed. More specifically, considering diversity and complementary information among multiple modalities, we incorporate autoencoding representation of complete and incomplete data into double contrastive learning to achieve learning consistency. Integrating GANs into the autoencoding process can not only take full advantage of new features of incomplete data, but also better generalise the model in the presence of high data missing rates. Experiments conducted on four extensively used data sets show that CIMIC-GAN outperforms state-of-the-art incomplete multi-View clustering methods.
资助项目Science and Technology Planning Project of Inner Mongolia Autonomous Region[2019GG372] ; National Science Foundation of China[61962045] ; National Science Foundation of China[62062055] ; National Science Foundation of China[61650205] ; National Science Foundation of China[61902382] ; National Science Foundation of China[61972381] ; Open Foundation of Inner Mongolia Key Laboratory of Discipline Inspection and Supervision[IMDBD2020017] ; Open Foundation of Inner Mongolia Key Laboratory of Discipline Inspection and Supervision[IMDBD2020018]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000865457600001
出版者WILEY
源URL[http://119.78.100.204/handle/2XEOYT63/19796]  
专题中国科学院计算技术研究所期刊论文
通讯作者Xu, Zhiwei
作者单位1.InnoPeak Technol Inc, Palo Alto, CA USA
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot 010080, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Jiatai,Xu, Zhiwei,Yang, Xuewen,et al. Self-supervised image clustering from multiple incomplete views via constrastive complementary generation[J]. IET COMPUTER VISION,2022:14.
APA Wang, Jiatai,Xu, Zhiwei,Yang, Xuewen,Guo, Dongjin,&Liu, Limin.(2022).Self-supervised image clustering from multiple incomplete views via constrastive complementary generation.IET COMPUTER VISION,14.
MLA Wang, Jiatai,et al."Self-supervised image clustering from multiple incomplete views via constrastive complementary generation".IET COMPUTER VISION (2022):14.

入库方式： OAI收割

来源：计算技术研究所

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