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您的检索式：作者名="CEN YiGang"
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| 1 | A new approach of conditions on δ_(2s)(Φ) for s-sparse recovery显示文摘In this paper,we provide a unified expression to obtain the conditions on the restricted isometry constantδ2s(Φ).These conditions cover the important results proposed by Candes et al.and each of them is a sufficient condition for sparse signal recovery.In the noiseless case,whenδ2s(Φ)satisfies any one of these conditions,the s-sparse signal can be exactly recovered via(l1)constrained minimization. | CEN YiGang ZHAO RuiZhen MIAO ZhenJiang CEN LiHui CUI LiHong | 2014 | Science China(Information Sciences)2014，57，4： | 1 |
| 2 | A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection显示文摘Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods. | Lanyao Zhang Shichao Kan Yigang Cen Xiaoling Chen Linna Zhang Yansen Huang | 2024 | Computers, Materials & Continua2024，78，2： | 0 |
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