no code implementations • 30 Apr 2024 • Lucas Grativol Ribeiro, Lubin Gauthier, Mathieu Leonardon, Jérémy Morlier, Antoine Lavrard-Meyer, Guillaume Muller, Virginie Fresse, Matthieu Arzel
This paper tackles the challenges of implementing few-shot learning on embedded systems, specifically FPGA SoCs, a vital approach for adapting to diverse classification tasks, especially when the costs of data acquisition or labeling prove to be prohibitively high.
1 code implementation • 23 Oct 2023 • Lucas Grativol Ribeiro, Mathieu Leonardon, Guillaume Muller, Virginie Fresse, Matthieu Arzel
Federated Learning (FL) is a promising distributed method for edge-level machine learning, particularly for privacysensitive applications such as those in military and medical domains, where client data cannot be shared or transferred to a cloud computing server.
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.
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.
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.
no code implementations • 18 Nov 2019 • Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia, Matthieu Arzel, Michel Jezequel
In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip.
no code implementations • 29 Dec 2018 • Ghouthi Boukli Hacene, Vincent Gripon, Matthieu Arzel, Nicolas Farrugia, Yoshua Bengio
Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection.
no code implementations • 4 Oct 2018 • Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia, Matthieu Arzel, Michel Jezequel
Deep learning-based methods have reached state of the art performances, relying on large quantity of available data and computational power.