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Data fusing and joint training for learning with noisy labels被引量:3
《Frontiers of Computer Science》2022年第6期63-70,共8页Yi WEI Mei XUE Xin LIU Pengxiang XU 
supported by SRC-Open Project of Research Center of Security Video and Image Processing Engineering Technology of Guizhou ([2020]001);Beijing Advanced Innovation Center for Intelligent Robots and Systems (2018IRS20);National Natural Science Foundation of China (Grant No.61973334).
It is well known that deep learning depends on a large amount of clean data.Because of high annotation cost,various methods have been devoted to annotating the data automatically.However,a larger number of the noisy l...
关键词:deep learning noisy labels data fusing 
Multi-objective particle swarm optimization by fusing multiple strategies被引量:1
《Journal of Measurement Science and Instrumentation》2022年第3期284-299,共16页XU Zhenxing ZHU Shuiran 
National Natural Science Foundation of China(No.61702006);Open Fund of Key laboratory of Anhui Higher Education Institutes(No.CS2021-ZD01)。
To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes t...
关键词:multi-objective particle swarm optimization(MOPSO) spatially crowding congestion distance differential guidance weight hierarchical selection of global optimum 
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