Parallel Learning: Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real

The virtual-to-real paradigm, i.e., training models on virtual data and then applying them to solve real-world problems, has attracted more and more attention from various domains by successfully alleviating the data shortage problem in machine learning. To summarize the advances in recent years, th...

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Vydané v:	IEEE/CAA journal of automatica sinica Ročník 10; číslo 3; s. 603 - 631
Hlavní autori:	Miao, Qinghai, Lv, Yisheng, Huang, Min, Wang, Xiao, Wang, Fei-Yue
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway Chinese Association of Automation (CAA) 01.03.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China%School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China State Key Laboratory for Manage-ment and Control of Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China%School of Artificial Intelligence,Anhui University,Hefei 266114,China,Qingdao Academy of Intelligent Industries,Qingdao 230031,China%State Key Laboratory for Manage-ment and Control of Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
Predmet:	Artificial intelligence Computer vision Data models Domains Machine learning Natural language processing parallel learning parallel systems Robotics sim-to-real syn-to-real Task analysis Taxonomy Training virtual-to-real syn-to-real parallel systems parallel learning virtual-to-real sim-to-real Machine learning
ISSN:	2329-9266, 2329-9274
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