AutoGCN-Toward Generic Human Activity Recognition With Neural Architecture Search

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...

Full description

Bibliographic Details
Main Authors: Felix Tempel, Espen Alexander F. Ihlen, Inga Strumke
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Neural architecture search
human activity recognition
graph convolution networks
AutoML
Online Access:https://ieeexplore.ieee.org/document/10472042/

Internet

https://ieeexplore.ieee.org/document/10472042/

Similar Items