计算机科学
分类
机器学习
人工智能
集合(抽象数据类型)
芯(光纤)
空格(标点符号)
点(几何)
数据科学
几何学
数学
电信
操作系统
程序设计语言
作者
Yaqing Wang,Quanming Yao,James T. Kwok,Lionel M. Ni
出处
期刊:ACM Computing Surveys
[Association for Computing Machinery]
日期:2020-06-12
卷期号:53 (3): 1-34
被引量:1243
摘要
Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to tackle this problem. Using prior knowledge, FSL can rapidly generalize to new tasks containing only a few samples with supervised information. In this article, we conduct a thorough survey to fully understand FSL. Starting from a formal definition of FSL, we distinguish FSL from several relevant machine learning problems. We then point out that the core issue in FSL is that the empirical risk minimizer is unreliable. Based on how prior knowledge can be used to handle this core issue, we categorize FSL methods from three perspectives: (i) data, which uses prior knowledge to augment the supervised experience; (ii) model, which uses prior knowledge to reduce the size of the hypothesis space; and (iii) algorithm, which uses prior knowledge to alter the search for the best hypothesis in the given hypothesis space. With this taxonomy, we review and discuss the pros and cons of each category. Promising directions, in the aspects of the FSL problem setups, techniques, applications, and theories, are also proposed to provide insights for future research. 1
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