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learning an identity distinguishable space for large scale face recognition

The Journal of China Universities of Posts and Telecommunications 2018年第1期25卷 54-61页

作者： Yue Ting Wang Hongbo Cheng Shiduan State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Implementing face recognition efficiently to real world large scale dataset presents great challenges to existing approaches. The method in this paper was proposed to learn an identity distinguishable space for large scale face recognition in MSR-Bing image recognition challenge （IRC）. Firstly, a deep convolutional neural network （CNN） was used to optimize a 128 B embedding for large scale face retrieval. The embedding was trained via using triplets of aligned face patches from FaceScrub and CASIA-WebFace datasets. Secondly, the evaluation of MSR-Bing IRC was conducted according to a cross-domain retrieval scheme. The real-time retrieval in this paper was benefited from the K-means clustering performed on the feature space of training data. Furthermore, a large scale similarity learning （LSSL） was applied on the relevant face images for learning a better identity space. A novel method for selecting similar pairs was proposed for LSSL. Compared with many existing networks of face recognition, the proposed model was lightweight and the retrieval method was promising as well.

关键词： face recognition convolutional neural network (CNN) triplet loss inception similarity learning

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SSRE:Cell Type Detection Based on Sparse Subspace Representation and similarity Enhancement

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Genomics, Proteomics & Bioinformatics 2021年第2期19卷 282-291页

作者： Zhenlan Liang Min Li Ruiqing Zheng Yu Tian Xuhua Yan Jin Chen Fang-Xiang Wu Jianxin Wang School of Computer Science and Engineering Central South UniversityChangsha 410083China College of Medicine University of KentuckyLexingtonKY 40536USA Division of Biomedical Engineering University of SaskatchewanSaskatoonSK S7N 5A9Canada

Accurate identification of cell types from single-cell RNA sequencing(scRNA-seq)data plays a critical role in a variety of scRNA-seq analysis *** task corresponds to solving an unsupervised clustering problem,in which the similarity measurement between cells affects the result *** many approaches for cell type identification have been proposed,the accuracy still needs to be *** this study,we proposed a novel single-cell clustering framework based on similarity learning,called *** models the relationships between cells based on subspace assumption,and generates a sparse representation of the cell-to-cell *** sparse representation retains the most similar neighbors for each ***,three classical pairwise similarities are incorporated with a gene selection and enhancement strategy to further improve the effectiveness of *** on ten real scRNA-seq datasets and five simulated datasets,SSRE achieved the superior performance in most cases compared to several state-of-the-art single-cell clustering *** addition,SSRE can be extended to visualization of scRNA-seq data and identification of differentially expressed *** matlab and python implementations of SSRE are available at https://***/CSUBioGroup/SSRE.

关键词： Single-cell RNA sequencing Clustering Cell type similarity learning Enhancement

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