A comprehensive perspective of contrastive self-supervised learning

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|
作者：
Songcan CHEN [1 ,2 ]
Chuanxing GENG [1 ,2 ]
机构：
[1] College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics
[2] MIIT Key Laboratory of Pattern Analysis and Machine
来源：
Frontiers of Computer Science | 2021年 / 04期
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摘要：
<正>Self-supervised learning(SSL), as a new unsupervised representation learning paradigm in machine learning, recently has received extensive attention, which is also regarded as the future of machine learning by the Turing Award winner,Le Cunn [1].
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页数：3
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