Few-Shot Learning in Wi-Fi-Based Indoor Positioning

Few-Shot Learning in Wi-Fi-Based Indoor Positioning

This paper explores the use of few-shot learning in Wi-Fi-based indoor positioning, utilizing convolutional neural networks (CNNs) combined with meta-learning techniques to enhance the accuracy and efficiency of positioning systems. The focus is on addressing the challenge of limited labeled data, a...

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Bibliographic Details
Main Authors:	Feng Xie, Soi Hoi Lam, Ming Xie, Cheng Wang
Format:	Article
Language:	English
Published:	MDPI AG 2024-09-01
Series:	Biomimetics
Subjects:	few-shot learning indoor positioning meta-learning cosine similarity limited labeled data few-sample learning
Online Access:	https://www.mdpi.com/2313-7673/9/9/551
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