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

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...

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Bibliographic Details
Published in:	Journal of computer science and technology 2022-06, Vol.37 (3), p.507-526
Main Authors:	Fang, Peng-Fei, Li, Xian, Yan, Yang, Zhang, Shuai, Kang, Qi-Yue, Li, Xiao-Fei, Lan, Zhen-Zhong
Format:	Article
Language:	eng
Subjects:	Algorithms Artificial Intelligence Computational linguistics Computer Science Computer vision Data Structures and Information Theory Information Systems Applications (incl.Internet) Language processing Machine learning Machine vision Natural language interfaces Natural language processing Polls & surveys Self-supervised learning Software Engineering Survey Surveys Theory of Computation
Online Access:	Get full text
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Summary:	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.
ISSN:	1000-9000 1860-4749