Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3145
Title: Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation
Authors: Lindamulage, A
Kodagoda, N
Reyal, S
Samarasinghe, P
Yogarajah, P
Keywords: Edge Inference
Head Pose estimation
Network Pruning
Optimisation
Quantisation
TensorFlow
Issue Date: 4-Nov-2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: A. Lindamulage, N. Kodagoda, S. Reyal, P. Samarasinghe and P. Yogarajah, "Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation," TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON), Hong Kong, Hong Kong, 2022, pp. 1-6, doi: 10.1109/TENCON55691.2022.9977637.
Series/Report no.: IEEE Region 10 Annual International Conference, Proceedings/TENCON;Volume 2022
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
URI: https://rda.sliit.lk/handle/123456789/3145
ISSN: 21593442
Appears in Collections:Department of Information Technology



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