计算机科学
判别式
人工智能
钥匙(锁)
机器学习
过程(计算)
无监督学习
计算机安全
操作系统
作者
Jie Gui,Tuo Chen,Qiong Cao,Zhenan Sun,Hao Luo,Dacheng Tao
出处
期刊:Cornell University - arXiv
日期:2023-01-01
被引量:2
标识
DOI:10.48550/arxiv.2301.05712
摘要
Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming. Self-supervised learning (SSL), a subset of unsupervised learning, aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has garnered significant attention recently, leading to the development of numerous related algorithms. However, there is a dearth of comprehensive studies that elucidate the connections and evolution of different SSL variants. This paper presents a review of diverse SSL methods, encompassing algorithmic aspects, application domains, three key trends, and open research questions. Firstly, we provide a detailed introduction to the motivations behind most SSL algorithms and compare their commonalities and differences. Secondly, we explore representative applications of SSL in domains such as image processing, computer vision, and natural language processing. Lastly, we discuss the three primary trends observed in SSL research and highlight the open questions that remain. A curated collection of valuable resources can be accessed at https://github.com/guijiejie/SSL.
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