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面向真实场景的图像超分辨率研究及应用 学位论文
专业学位硕士, 沈阳: 中国科学院沈阳自动化研究所, 2021
Authors:  杨琦瑞
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卷积神经网络  生成对抗网络  图像超分辨率  感知损失  图像质量评价  
多源数据异常检测及应用研究 学位论文
博士, 沈阳: 中国科学院沈阳自动化研究所, 2021
Authors:  侯冬冬
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异常检测  工业数据  时序数据  视频数据  多视图数据  
Representative Task Self-Selection for Flexible Clustered Lifelong Learning 期刊论文
IEEE Transactions on Neural Networks and Learning Systems, 2021, 页码: 1-15
Authors:  Sun G(孙干);  Cong Y(丛杨);  Wang QQ(王倩倩);  Zhong BN(钟必能);  Fu Y(付昀)
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Clustering analysis  lifelong machine learning  multitask learning (MTL)  transfer learning  
Long-term correlations and multifractality of toll-free calls in China 期刊论文
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2021, 卷号: 567, 页码: 1-12
Authors:  Gui J(桂珺);  Zheng ZY(郑泽宇);  Fu DZ(付殿峥);  Fu Y(傅洋);  Liu Z(刘智)
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Calling activity  Long-term correlation  Multifractality  MFDFA  Scaling behavior  
基于数据驱动的自主式水下潜水器故障检测方法研究与应用 学位论文
硕士, 沈阳: 中国科学院沈阳自动化研究所, 2020
Authors:  杨宗圣
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自主式水下潜水器  故障检测  主元分析  注意力机制  大数据  
Image Clustering Based on Multi-Scale Deep Maximize Mutual Information and Self-Training Algorithm 期刊论文
IEEE ACCESS, 2020, 卷号: 8, 页码: 160285-160296
Authors:  Yu SQ(余思泉);  Shen GP(沈贵萍);  Wang PY(王佩瑶);  Wang YN(王宇宁);  Wu CD(吴成东)
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Clustering algorithms  Mutual information  Image representation  Neural networks  Clustering methods  Feature extraction  Task analysis  Representation learning  image clustering  mutual information maximization  self-training algorithm  
基于在线学习的多任务方法研究 学位论文
博士, 沈阳: 中国科学院沈阳自动化研究所, 2019
Authors:  孙干
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终身机器学习  多任务学习  在线学习  度量学习  迁移学习  
RTSNet: Real-Time Semantic Segmentation Network for Outdoor Scenes 会议论文
Proceedings of 9th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, Suzhou, China, July 29 - August 2, 2019
Authors:  Ma, Mingyu;  Zou FS(邹风山);  Xu F(徐方);  Song JL(宋吉来)
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semantic segmentation  real-time  outdoor scenes  RTSNet  mean intersection-over-union  
基于RGB-D相机的稠密即时定位与地图构建方法研究 学位论文
博士, 沈阳: 中国科学院沈阳自动化研究所, 2018
Authors:  付兴银
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稠密即时定位地图构建  面元  Icp  关键帧  Rgb-d  
A Experimental Study to Invariance of Several Groups Action to the Input of Residual Networks 会议论文
Proceeding of the 2018 IEEE International Conference on Information and Automation, Wuyishan, China, August 11-13, 2018
Authors:  Yang XY(杨秀义);  Han Z(韩志);  Tang YD(唐延东);  Han JD(韩建达)
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Invariance  Residual network  Group action