A concise review of recent few-shot meta-learning methods

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 a...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 456; pp. 463 - 468
Main Authors Li, Xiaoxu, Sun, Zhuo, Xue, Jing-Hao, Ma, Zhanyu
Format Journal Article
LanguageEnglish
Published Elsevier B.V 07.10.2021
Subjects
Deep Neural Networks
Few-shot Learning
Image Classification
Meta Learning
Small-sample Learning
Image Classification
Few-shot Learning
Meta Learning
Small-sample Learning
Deep Neural Networks
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.05.114