Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios

Multi-view clustering (MVC) aims at exploring category structures among multi-view data in self-supervised manners. Multiple views provide more information than single views and thus existing MVC methods can achieve satisfactory performance. However, their performance might seriously degenerate when...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 22957 - 22966
Hlavní autori:	Xu, Jie, Ren, Yazhou, Wang, Xiaolong, Feng, Lei, Zhang, Zheng, Niu, Gang, Zhu, Xiaofeng
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 16.06.2024
Predmet:	Clustering methods Computational modeling Computer vision Multi-View Learning Noise robustness Noisy-View Drawback Refining Representation learning Self-Supervised Clustering Sensitivity
ISSN:	1063-6919
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