Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation

Autor:	Asiri Lindamulage, Nuwan Kodagoda, Shyam Reyal, Pradeepa Samarasinghe, Pratheepan Yogarajah
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition
Popis:	Magnitude-based pruning is a technique used to optimise deep learning models for edge inference. We have achieved over 75% model size reduction with a higher accuracy than the original multi-output regression model for head-pose estimation. Conference:- in TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c14951277f38c9b0a8e1a549ea6cbe56 http://arxiv.org/abs/2302.00592 Zobrazit plný text záznamu