AutoGCN-Toward Generic Human Activity Recognition With Neural Architecture Search

This paper introduces AutoGCN, a generic Neural Architecture Search (NAS) algorithm for Human Activity Recognition (HAR) using Graph Convolution Networks (GCNs). HAR has enjoyed increased attention due to advances in deep learning, increased data availability, and enhanced computational capabilities...

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Hlavní autoři:	Felix Tempel, Espen Alexander F. Ihlen, Inga Strumke
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2024-01-01
Edice:	IEEE Access
Témata:	Neural architecture search human activity recognition graph convolution networks AutoML
On-line přístup:	https://ieeexplore.ieee.org/document/10472042/
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