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Adaptive super -resolution for person re-identification with low-resolution images 期刊论文
PATTERN RECOGNITION, 2021, 卷号: 114, 页码: 12
Authors:  Han, Ke;  Huang, Yan;  Song, Chunfeng;  Wang, Liang;  Tan, Tieniu
Favorite  |  View/Download:8/0  |  Submit date:2021/05/06
Person re-identification  Super-resolution  Body regions  Adaptive feature integration  
A Vibration Control Method for Hybrid-Structured Flexible Manipulator Based on Sliding Mode Control and Reinforcement Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 卷号: 32, 期号: 2, 页码: 841-852
Authors:  Long, Teng;  Li, En;  Hu, Yunqing;  Yang, Lei;  Fan, Junfeng;  Liang, Zize;  Guo, Rui
Favorite  |  View/Download:4/0  |  Submit date:2021/03/29
Vibrations  Mathematical model  Manipulator dynamics  Torque  Robustness  Neural networks  Hybrid-structured flexible manipulator  reinforcement learning  sliding mode control  vibration control method  
Automatic Detection and Location of Weld Beads With Deep Convolutional Neural Networks 期刊论文
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 卷号: 70, 页码: 12
Authors:  Yang, Lei;  Fan, Junfeng;  Liu, Yanhong;  Li, En;  Peng, Jinzhu;  Liang, Zize
Favorite  |  View/Download:8/0  |  Submit date:2021/03/08
Data augmentation  deep convolutional network model  object location  samples updating  semantic segmentation  weld bead  
Few-Shot Learning via Feature Hallucination with Variational Inference 会议论文
, 线上会议, 2021-1
Authors:  Luo QX(罗沁轩);  Wang LF(汪凌峰);  Lv JG(吕京国);  Xiang SM(向世明);  Pan CH(潘春洪)
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Disentangled Item Representation for Recommender Systems 期刊论文
Transactions on Intelligent Systems and Technology (TIST), 2021, 卷号: 0, 期号: 0, 页码: 0
Authors:  Cui Zeyu;  Yu Feng;  Wu Shu;  Liu Qiang;  Wang Liang
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Representation learning  Recommender systems  Attribute disentangling  
A Review on State-of-the-Art Power Line Inspection Techniques 期刊论文
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 卷号: 69, 期号: 12, 页码: 9350-9365
Authors:  Yang, Lei;  Fan, Junfeng;  Liu, Yanhong;  Li, En;  Peng, Jinzhu;  Liang, Zize
Favorite  |  View/Download:12/0  |  Submit date:2021/01/06
Inspection  Poles and towers  Task analysis  Insulators  Smart grids  Feature extraction  Inspection methods  inspection platforms  inspection sensors  inspection task  power line inspection  
A Fuzzy Deep Model Based on Fuzzy Restricted Boltzmann Machines for High-Dimensional Data Classification 期刊论文
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2020, 卷号: 28, 期号: 7, 页码: 1344-1355
Authors:  Feng, Shuang;  Chen, C. L. Philip;  Zhang, Chun-Yang
Favorite  |  View/Download:33/0  |  Submit date:2020/08/03
Data models  Training  Training data  Fuzzy neural networks  Databases  Neural networks  Classification  fuzzy deep model  fuzzy restricted Boltzmann machine (FRBM)  hybrid learning  
Generative and Discriminative Fuzzy Restricted Boltzmann Machine Learning for Text and Image Classification 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 卷号: 50, 期号: 5, 页码: 2237-2248
Authors:  Chen, C. L. Philip;  Feng, Shuang
Favorite  |  View/Download:30/0  |  Submit date:2020/06/22
Data models  Training  Neurons  Image reconstruction  Feature extraction  Computational modeling  Cybernetics  Discriminative learning  fuzzy number  Gaussian fuzzy restricted Boltzmann machine (GFRBM)  image classification  
Deep Self-Evolution Clustering 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 卷号: 42, 期号: 4, 页码: 809-823
Authors:  Chang, Jianlong;  Meng, Gaofeng;  Wang, Lingfeng;  Xiang, Shiming;  Pan, Chunhong
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Task analysis  Unsupervised learning  Training  Clustering methods  Pattern analysis  Clustering  deep self-evolution clustering  self-evolution clustering training  deep unsupervised learning  
Frame-GAN: Increasing the frame rate of gait videos with generative adversarial networks 期刊论文
NEUROCOMPUTING, 2020, 卷号: 380, 页码: 95-104
Authors:  Xue, Wei;  Ai, Hong;  Sun, Tianyu;  Song, Chunfeng;  Huang, Yan;  Wang, Liang
Favorite  |  View/Download:60/0  |  Submit date:2020/03/30
Gait recognition  Generative adversarial networks  Metric learning  Deep learning