Nathan Hubens | DeepAI

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Featured Co-authors

Thierry Dutoit
41 publications
Stéphane Dupont
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Jean-Benoit Delbrouck
20 publications
Noé Tits
14 publications
Matei Mancas
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Bernard Gosselin
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Antoine Maiorca
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Marius Preda
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Titus Zaharia
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Omar Seddati
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Victor Delvigne
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∙ 05/30/2023

A Recipe for Efficient SBIR Models: Combining Relative Triplet Loss with Batch Normalization and Knowledge Distillation

Sketch-Based Image Retrieval (SBIR) is a crucial task in multimedia retr...

0 Omar Seddati, et al. ∙

∙ 03/20/2023

Induced Feature Selection by Structured Pruning

The advent of sparsity inducing techniques in neural networks has been o...

2 Nathan Hubens, et al. ∙

∙ 07/03/2022

FasterAI: A Lightweight Library for Creating Sparse Neural Networks

FasterAI is a PyTorch-based library, aiming to facilitate the utilizatio...

45 Nathan Hubens, et al. ∙

∙ 03/11/2022

Improve Convolutional Neural Network Pruning by Maximizing Filter Variety

Neural network pruning is a widely used strategy for reducing model stor...

9 Nathan Hubens, et al. ∙

∙ 01/11/2022

Towards Lightweight Neural Animation : Exploration of Neural Network Pruning in Mixture of Experts-based Animation Models

In the past few years, neural character animation has emerged and offere...

11 Antoine Maiorca, et al. ∙

∙ 01/11/2022

Where Is My Mind (looking at)? Predicting Visual Attention from Brain Activity

Visual attention estimation is an active field of research at the crossr...

19 Victor Delvigne, et al. ∙

∙ 12/15/2021

An Experimental Study of the Impact of Pre-training on the Pruning of a Convolutional Neural Network

In recent years, deep neural networks have known a wide success in vario...

3 Nathan Hubens, et al. ∙

∙ 07/05/2021

One-Cycle Pruning: Pruning ConvNets Under a Tight Training Budget

Introducing sparsity in a neural network has been an efficient way to re...

1 Nathan Hubens, et al. ∙

∙ 10/08/2019

Modulated Self-attention Convolutional Network for VQA

As new data-sets for real-world visual reasoning and compositional quest...

0 Jean-Benoit Delbrouck, et al. ∙

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