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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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| File | Description | Size | Format | |
|---|---|---|---|---|
| Comparative_Study_of_Parameter_Selection_for_Enhanced_Edge_Inference_for_a_Multi-Output_Regression_model_for_Head_Pose_Estimation.pdf Until 2050-12-31 | 833.41 kB | Adobe PDF | View/Open Request a copy |
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