Lanzhou University of Technology Institutional Repository (LUT_IR)
A concise review of recent few-shot meta-learning methods | |
Li, Xiaoxu1; Sun, Zhuo2; Xue, Jing-Hao2; Ma, Zhanyu3 | |
2021-10-07 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
卷号 | 456页码:463-468 |
摘要 | Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge. In this short communication, we give a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics. We conclude this review with some vital current challenges and future prospects in few-shot meta-learning. (c) 2020 Elsevier B.V. All rights reserved. |
关键词 | Meta Learning Few-shot Learning Image Classification Deep Neural Networks Small-sample Learning |
DOI | 10.1016/j.neucom.2020.05.114 |
收录类别 | EI ; CCF ; SCOPUS ; SCIE |
语种 | 英语 |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000687472700006 |
出版者 | ELSEVIER |
EI入藏号 | 20204609496743 |
EI主题词 | Learning systems |
EI分类号 | 723.5 Computer Applications |
来源库 | WOS |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/148493 |
专题 | 兰州理工大学 |
通讯作者 | Xue, Jing-Hao |
作者单位 | 1.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou, Peoples R China; 2.UCL, Dept Stat Sci, London, England; 3.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Pattern Recognit & Intelligent Syst Lab, Beijing, Peoples R China |
第一作者单位 | 兰州理工大学 |
第一作者的第一单位 | 兰州理工大学 |
推荐引用方式 GB/T 7714 | Li, Xiaoxu,Sun, Zhuo,Xue, Jing-Hao,et al. A concise review of recent few-shot meta-learning methods[J]. NEUROCOMPUTING,2021,456:463-468. |
APA | Li, Xiaoxu,Sun, Zhuo,Xue, Jing-Hao,&Ma, Zhanyu.(2021).A concise review of recent few-shot meta-learning methods.NEUROCOMPUTING,456,463-468. |
MLA | Li, Xiaoxu,et al."A concise review of recent few-shot meta-learning methods".NEUROCOMPUTING 456(2021):463-468. |
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