Journal of Computer Science and Technology ›› 2022, Vol. 37 ›› Issue (3): 507-526.doi: 10.1007/s11390-022-2158-x

Special Issue: Surveys; Artificial Intelligence and Pattern Recognition

• Special Section on Self-Learning with Deep Neural Networks • Previous Articles     Next Articles

Connecting the Dots in Self-Supervised Learning: A Brief Survey for Beginners

Peng-Fei Fang1,2 (方鹏飞), Xian Li1 (李贤), Yang Yan1,3 (燕阳), Shuai Zhang1,3 (章帅), Qi-Yue Kang1 (康启越), Xiao-Fei Li1 (李晓飞), and Zhen-Zhong Lan1 (蓝振忠)        

  1. 1School of Engineering, WestLake University, Hangzhou 310030, China
    2College of Engineering and Computer Science, Australian National University, Canberra, ACT 2601, Australia
    3College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  • Received:2022-01-15 Revised:2022-05-12 Accepted:2022-05-18 Online:2022-05-30 Published:2022-05-30
  • Contact: Peng-Fei Fang E-mail:fangpengfei@westlake.edu.cn
  • About author:Peng-Fei Fang received his B.E. degree in automation from Hangzhou Dianzi University (HDU), Hangzhou, in 2014, and his M.E. degree in mechatronics from Australian National University (ANU), Canberra, in 2017. He is currently pursuing his joint Ph.D. degree with ANU and the Data61-CSIRO. He is also a visiting scholar with Westlake University, Hangzhou. His research interests include computer vision and machine learning.
  • Supported by:
    This work was supported by the Key Research and Development Program of Zhejiang Province under Grant No. 2021C03139.

The artificial intelligence (AI) community has recently made tremendous progress in developing self-supervised learning (SSL) algorithms that can learn high-quality data representations from massive amounts of unlabeled data. These methods brought great results even to the fields outside of AI. Due to the joint efforts of researchers in various areas, new SSL methods come out daily. However, such a sheer number of publications make it difficult for beginners to see clearly how the subject progresses. This survey bridges this gap by carefully selecting a small portion of papers that we believe are milestones or essential work. We see these researches as the "dots" of SSL and connect them through how they evolve. Hopefully, by viewing the connections of these dots, readers will have a high-level picture of the development of SSL across multiple disciplines including natural language processing, computer vision, graph learning, audio processing, and protein learning.

Key words: artificial intelligence (AI); dot; self-supervised learning (SSL); survey;

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