Z
Zhibin Wan
Researcher at Tianjin University
Publications - 4
Citations - 228
Zhibin Wan is an academic researcher from Tianjin University. The author has contributed to research in topics: Feature learning & Overfitting. The author has an hindex of 2, co-authored 4 publications receiving 116 citations.
Papers
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Journal ArticleDOI
Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning
Hengyuan Kang,Liming Xia,Fuhua Yan,Zhibin Wan,Feng Shi,Huan Yuan,Huiting Jiang,Dijia Wu,He Sui,Changqing Zhang,Dinggang Shen +10 more
TL;DR: In this article, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability, while the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP).
Journal ArticleDOI
Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning
Hengyuan Kang,Liming Xia,Fuhua Yan,Zhibin Wan,Feng Shi,Huan Yuan,Huiting Jiang,Dijia Wu,He Sui,Changqing Zhang,Dinggang Shen +10 more
TL;DR: This study proposes to conduct the diagnosis of COVID-19 with a series of features extracted from CT images, and shows that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.
Proceedings Article
Multi-View Information-Bottleneck Representation Learning.
TL;DR: In this article, a collaborative multi-view information bottleneck network (CMIB-Nets) is proposed to integrate the shared representation among different views and the view-specific representation of each view.
Proceedings ArticleDOI
Cross-View Equivariant Auto-Encoder
TL;DR: A novel unsupervised multi-view representation learning model termed as Cross-View Equivariant Auto-Encoder (CVE-AE), which jointly conducts data re-construction with view-specific autoencoder for information preservation within each view, and transformation reconstruction with transformation decoder for correlations preservation across different views is proposed.