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Can I read Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity on EtoBox?
Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity by Jiangmeng Li; Wenwen Qiang; Changwen Zheng; Bing Su; Farid Razzak; Ji-Rong Wen; Hui Xiong is a Computer Science article available to read on EtoBox.
What is Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity about?
While self-supervised learning techniques are often used to mine hidden knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a new multi-view self-supervised learning method, namely consistency and complementarity network (CoCoNet), to comprehensively learn global inter-view consistent and local cross-view complementarity-preserving representations from multiple views. To capture crucial common knowledge which is implicitly shared among views, CoCoNet employs a global consistency module that aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric based on the generalized sliced Wasserstein distance. To incorporate cross-view complementary information, CoCoNet proposes a heuristic complementarity-aware contrastive Manuscript
Who reads Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Jiangmeng Li; Wenwen Qiang; Changwen Zheng; Bing Su; Farid Razzak; Ji-Rong Wen; Hui Xiong
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Published
- 2022
- Language
- EN
- Field
- Computer Science (Physical Sciences)