Impact of Disentanglement on Pruning Neural Networks

descriptionPublicationkeyboard_double_arrow_right Conference object , Other literature type , Article , Preprint 01 Jan 2023Embargo end date: 01 Jul 2023 Luxembourg Publisher:ZenodoJournal:CoRR, volume abs/2307.09994

Authors: Carl Shneider; Peyman Rostami; Anis Kacem; Nilotpal Sinha; Abdelrahman Shabayek; Djamila Aouada;

doi: 10.5281/zenodo.8060029 , 10.5281/zenodo.8060030 , 10.48550/arxiv.2307.09994

arXiv: 2307.09994

Impact of Disentanglement on Pruning Neural Networks

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Abstract

Efficient model compression techniques are required to deploy deep neural networks (DNNs) on edge devices for task specific objectives. A variational autoencoder (VAE) framework is combined with a pruning criterion to investigate the impact of having the network learn disentangled representations on the pruning process for the classification task. Poster from the Computer Vision, Imaging, and Machine Intelligence Research Group (CVI2) at SnT, University of Luxembourg. Selected for poster presentation during the first edition of the International Symposium on Computational Sensing ISCS23 in Luxembourg.

This work was funded by the Luxembourg National Research Fund (FNR) under the project reference C21/IS/15965298/ELITE.

Country

Luxembourg

Related Organizations

University of Luxembourg
Luxembourg

Keywords

Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Physique, chimie, mathématiques & sciences de la terre, Computer Vision and Pattern Recognition (cs.CV), Ingénierie électrique & électronique, Neural Network Pruning, Computer Science - Computer Vision and Pattern Recognition, Variational Autoencoders, Deep learning, Edge computing, Edge Devices, Engineering, computing & technology, Ingénierie, informatique & technologie, Machine Learning (cs.LG), Disentangled latent representation, Variational Autoencoder, Deep Learning, Physical, chemical, mathematical & earth Sciences, FOS: Electrical engineering, electronic engineering, information engineering, Neural Network Compression, Electrical Engineering and Systems Science - Signal Processing, Electrical & electronics engineering

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