Search Results for author: Ghouthi Boukli Hacene

Found 17 papers, 5 papers with code

Transfer Incremental Learning using Data Augmentation

no code implementations4 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.

Data Augmentation Incremental Learning

Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks

no code implementations29 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.

Quantization

Attention Based Pruning for Shift Networks

1 code implementation29 May 2019 Ghouthi Boukli Hacene, Carlos Lassance, Vincent Gripon, Matthieu Courbariaux, Yoshua Bengio

In many application domains such as computer vision, Convolutional Layers (CLs) are key to the accuracy of deep learning methods.

Object Recognition

Deep geometric knowledge distillation with graphs

1 code implementation8 Nov 2019 Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega

Specifically we introduce a graph-based RKD method, in which graphs are used to capture the geometry of latent spaces.

Knowledge Distillation

Training Modern Deep Neural Networks for Memory-Fault Robustness

no code implementations23 Nov 2019 Ghouthi Boukli Hacene, François Leduc-Primeau, Amal Ben Soussia, Vincent Gripon, François Gagnon

Because deep neural networks (DNNs) rely on a large number of parameters and computations, their implementation in energy-constrained systems is challenging.

BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization

no code implementations8 Feb 2020 Miloš Nikolić, Ghouthi Boukli Hacene, Ciaran Bannon, Alberto Delmas Lascorz, Matthieu Courbariaux, Yoshua Bengio, Vincent Gripon, Andreas Moshovos

Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer quantization, yielding both execution time and energy benefits on existing hardware designs that support short bitlengths.

Quantization

ThriftyNets : Convolutional Neural Networks with Tiny Parameter Budget

no code implementations20 Jul 2020 Guillaume Coiffier, Ghouthi Boukli Hacene, Vincent Gripon

Typical deep convolutional architectures present an increasing number of feature maps as we go deeper in the network, whereas spatial resolution of inputs is decreased through downsampling operations.

DecisiveNets: Training Deep Associative Memories to Solve Complex Machine Learning Problems

no code implementations2 Dec 2020 Vincent Gripon, Carlos Lassance, Ghouthi Boukli Hacene

Learning deep representations to solve complex machine learning tasks has become the prominent trend in the past few years.

BIG-bench Machine Learning

Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning

1 code implementation24 Nov 2023 Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Giulia Lioi, Lukas Mauch, Fabien Cardinaux, Ghouthi Boukli Hacene

In this paper, we present a novel approach that leverages text-derived statistics to predict the mean and covariance of the visual feature distribution for each class.

Few-Shot Learning

A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models

no code implementations20 Jan 2024 Reda Bensaid, Vincent Gripon, François Leduc-Primeau, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux

In this study, we delve into the quest for identifying the most effective vision foundation models for few-shot semantic segmentation, a critical task in computer vision.

Few-Shot Semantic Segmentation Segmentation +1

Cannot find the paper you are looking for? You can Submit a new open access paper.