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Volume 10 Issue 3
Mar.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Q. H. Miao, Y. S. Lv, M. Huang, X. Wang, and F.-Y. Wang, “Parallel learning: Overview and perspective for computational learning across Syn2Real and Sim2Real,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 603–631, Mar. 2023. doi: 10.1109/JAS.2023.123375
Citation: Q. H. Miao, Y. S. Lv, M. Huang, X. Wang, and F.-Y. Wang, “Parallel learning: Overview and perspective for computational learning across Syn2Real and Sim2Real,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 603–631, Mar. 2023. doi: 10.1109/JAS.2023.123375

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

doi: 10.1109/JAS.2023.123375
Funds:  This work was partially supported by the National Key Research and Development Program of China (2020YFB2104001), the National Natural Science Foundation of China (62271485, 61903363, U1811463), and Open Project of the State Key Laboratory for Management and Control of Complex Systems (20220117)
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  • 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, this survey comprehensively reviews the literature, from the viewport of parallel intelligence. First, an extended parallel learning framework is proposed to cover main domains including computer vision, natural language processing, robotics, and autonomous driving. Second, a multi-dimensional taxonomy is designed to organize the literature in a hierarchical structure. Third, the related virtual-to-real works are analyzed and compared according to the three principles of parallel learning known as description, prediction, and prescription, which cover the methods for constructing virtual worlds, generating labeled data, domain transferring, model training and testing, as well as optimizing the strategies to guide the task-oriented data generator for better learning performance. Key issues remained in virtual-to-real are discussed. Furthermore, the future research directions from the viewpoint of parallel learning are suggested.

     

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    Highlights

    • An extended Parallel Learning framework covering main machine learning tasks including computer vision, natural lan- guage processing, robotics and autonomous driving
    • A systematical survey of the existing methods via virtual- to-real paradigm from the viewpoints of parallel learning
    • A multi-dimensional and multi-level taxonomy of virtual- to-real methods
    • A discussion about the current situation, and the main challenges and opportunities for future work

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