Pruning meta-trained networks for on-device adaptation

Adapting neural networks to unseen tasks with few training samples on resource-constrained devices benefits various Internet-of-Things applications. Such neural networks should learn the new tasks in few shots and be compact in size. Meta-learning enables few-shot learning, yet the meta-trained netw...

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Bibliographic Details
Main Authors: GAO, Dawei, HE, Xiaoxi, ZHOU, Zimu, TONG, Yongxin, THIELE, Lothar
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/sis_research/6702
https://ink.library.smu.edu.sg/context/sis_research/article/7705/viewcontent/cikm21_gao.pdf
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Institution: Singapore Management University
Language: English
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