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

arXiv 2302.00592 ↗First posted 28 Dec 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 28 Dec 2022.

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9 of 17 references.

References · 9

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  2. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  3. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  4. 2017To prune, or not to prune: exploring the efficacy of pruning for model compressioncited by 596
  5. 2021Pruning and Quantization for Deep Neural Network Acceleration: A Surveycited by 259
  6. 2018Face Alignment in Full Pose Range: A 3D Total Solutioncited by 147
  7. 2018AI Benchmark: Running Deep Neural Networks on Android Smartphonescited by 75
  8. 2019Neural Network Inference on Mobile SoCscited by 17
  9. 2022DEFER: Distributed Edge Inference for Deep Neural Networkscited by 3

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