Li, X;
Sun, Z;
Xue, J;
Ma, Z;
(2021)
A concise review of recent few-shot meta-learning methods.
Neurocomputing
, 456
pp. 463-468.
10.1016/j.neucom.2020.05.114.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | A concise review of recent few-shot meta-learning methods |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.neucom.2020.05.114 |
| Publisher version: | https://doi.org/10.1016/j.neucom.2020.05.114 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Meta Learning, Few-shot Learning, Image Classification, Deep Neural Networks, Small-sample Learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10103880 |
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