ThinResNet: A New Baseline for Structured Convolutional Networks Pruning

1 code implementation • 22 Sep 2023 • Hugo Tessier, Ghouti Boukli Hacene, Vincent Gripon

Pruning is a compression method which aims to improve the efficiency of neural networks by reducing their number of parameters while maintaining a good performance, thus enhancing the performance-to-cost ratio in nontrivial ways.

Energy Consumption Analysis of pruned Semantic Segmentation Networks on an Embedded GPU

1 code implementation • 13 Jun 2022 • Hugo Tessier, Vincent Gripon, Mathieu Léonardon, Matthieu Arzel, David Bertrand, Thomas Hannagan

Deep neural networks are the state of the art in many computer vision tasks.

Autonomous Driving Semantic Segmentation

Leveraging Structured Pruning of Convolutional Neural Networks

1 code implementation • 13 Jun 2022 • Hugo Tessier, Vincent Gripon, Mathieu Léonardon, Matthieu Arzel, David Bertrand, Thomas Hannagan

Structured pruning is a popular method to reduce the cost of convolutional neural networks, that are the state of the art in many computer vision tasks.

Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding

no code implementations • 9 Mar 2022 • Yassine El Ouahidi, Hugo Tessier, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon

In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals.

Rethinking Weight Decay For Efficient Neural Network Pruning

1 code implementation • 20 Nov 2020 • Hugo Tessier, Vincent Gripon, Mathieu Léonardon, Matthieu Arzel, Thomas Hannagan, David Bertrand

Introduced in the late 1980s for generalization purposes, pruning has now become a staple for compressing deep neural networks.

Efficient Neural Network Network Pruning