Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

Meta-learning has been the most common framework for few-shot learning in recent years. It learns the model from collections of few-shot classification tasks, which is believed to have a key advantage of making the training objective consistent with the testing objective. However, some recent works...

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Vydané v:	Proceedings / IEEE International Conference on Computer Vision s. 9042 - 9051
Hlavní autori:	Chen, Yinbo, Liu, Zhuang, Xu, Huijuan, Darrell, Trevor, Wang, Xiaolong
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.10.2021
Predmet:	Classification algorithms Codes Computer vision Measurement Task analysis Training Transfer/Low-shot/Semi/Unsupervised Learning
ISSN:	2380-7504
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