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

Abstract

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)

References in corpus (2)

• To prune, or not to prune: exploring the efficacy of pruning for model compression
• DEFER: Distributed Edge Inference for Deep Neural Networks