Search Results for author: Vincent Gripon

Found 71 papers, 26 papers with code

LLM meets Vision-Language Models for Zero-Shot One-Class Classification

no code implementations31 Mar 2024 Yassir Bendou, Giulia Lioi, Bastien Pasdeloup, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux, Vincent Gripon

In this setting, only the label of the target class is available, and the goal is to discriminate between positive and negative query samples without requiring any validation example from the target task.

One-Class Classification

Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding

no code implementations15 Mar 2024 Yassine El Ouahidi, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon

In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision.

Brain Decoding Motor Imagery

On Transfer in Classification: How Well do Subsets of Classes Generalize?

no code implementations6 Mar 2024 Raphael Baena, Lucas Drumetz, Vincent Gripon

In classification, it is usual to observe that models trained on a given set of classes can generalize to previously unseen ones, suggesting the ability to learn beyond the initial task.

Few-Shot Learning Transfer Learning

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

1 code implementation20 Jan 2024 Reda Bensaid, Vincent Gripon, François Leduc-Primeau, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux

In recent years, the rapid evolution of computer vision has seen the emergence of various foundation models, each tailored to specific data types and tasks.

Few-Shot Semantic Segmentation Segmentation +1

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

ThinResNet: A New Baseline for Structured Convolutional Networks Pruning

1 code implementation22 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.

A Strong and Simple Deep Learning Baseline for BCI MI Decoding

1 code implementation11 Sep 2023 Yassine El Ouahidi, Vincent Gripon, Bastien Pasdeloup, Ghaith Bouallegue, Nicolas Farrugia, Giulia Lioi

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI.

EEG Motor Imagery +1

Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach

1 code implementation16 Jan 2023 Yassir Bendou, Lucas Drumetz, Vincent Gripon, Giulia Lioi, Bastien Pasdeloup

Then, we introduce a downstream classifier meant to exploit the presence of multiple objects to improve the performance of few-shot classification, in the case of extreme settings where only one shot is given for its class.

Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings

no code implementations23 Sep 2022 Aymane Abdali, Vincent Gripon, Lucas Drumetz, Bartosz Boguslawski

We consider a novel formulation of the problem of Active Few-Shot Classification (AFSC) where the objective is to classify a small, initially unlabeled, dataset given a very restrained labeling budget.

Active Learning

Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification

no code implementations18 Sep 2022 Yuqing Hu, Stéphane Pateux, Vincent Gripon

Transductive Few-Shot learning has gained increased attention nowadays considering the cost of data annotations along with the increased accuracy provided by unlabelled samples in the domain of few shot.

Bayesian Inference Clustering +4

Preserving Fine-Grain Feature Information in Classification via Entropic Regularization

1 code implementation7 Aug 2022 Raphael Baena, Lucas Drumetz, Vincent Gripon

In this paper we are interested in the problem of solving fine-grain classification or regression, using a model trained on coarse-grain labels only.

Classification Few-Shot Learning +3

Leveraging Structured Pruning of Convolutional Neural Networks

1 code implementation13 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 implementations9 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.

EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients

2 code implementations24 Jan 2022 Yassir Bendou, Yuqing Hu, Raphael Lafargue, Giulia Lioi, Bastien Pasdeloup, Stéphane Pateux, Vincent Gripon

Few-shot learning aims at leveraging knowledge learned by one or more deep learning models, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available.

Few-Shot Image Classification Few-Shot Learning

Preventing Manifold Intrusion with Locality: Local Mixup

1 code implementation12 Jan 2022 Raphael Baena, Lucas Drumetz, Vincent Gripon

Mixup is a data-dependent regularization technique that consists in linearly interpolating input samples and associated outputs.

Image Classification

Graphs as Tools to Improve Deep Learning Methods

no code implementations8 Oct 2021 Carlos Lassance, Myriam Bontonou, Mounia Hamidouche, Bastien Pasdeloup, Lucas Drumetz, Vincent Gripon

This chapter is composed of four main parts: tools for visualizing intermediate layers in a DNN, denoising data representations, optimizing graph objective functions and regularizing the learning process.


Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification

1 code implementation23 Aug 2021 Myriam Bontonou, Nicolas Farrugia, Vincent Gripon

It is very common to face classification problems where the number of available labeled samples is small compared to their dimension.

Using Deep Neural Networks to Predict and Improve the Performance of Polar Codes

1 code implementation11 May 2021 Mathieu Léonardon, Vincent Gripon

Polar codes can theoretically achieve very competitive Frame Error Rates.

Inferring Graph Signal Translations as Invariant Transformations for Classification Tasks

no code implementations18 Feb 2021 Raphael Baena, Lucas Drumetz, Vincent Gripon

The field of Graph Signal Processing (GSP) has proposed tools to generalize harmonic analysis to complex domains represented through graphs.

Classification General Classification

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

Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs

1 code implementation25 Nov 2020 Carlos Lassance, Louis Béthune, Myriam Bontonou, Mounia Hamidouche, Vincent Gripon

Measuring the generalization performance of a Deep Neural Network (DNN) without relying on a validation set is a difficult task.

Rethinking Weight Decay For Efficient Neural Network Pruning

1 code implementation20 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

Representing Deep Neural Networks Latent Space Geometries with Graphs

no code implementations14 Nov 2020 Carlos Lassance, Vincent Gripon, Antonio Ortega

However, when processing a batch of inputs concurrently, the corresponding set of intermediate representations exhibit relations (what we call a geometry) on which desired properties can be sought.

Few-shot Decoding of Brain Activation Maps

1 code implementation23 Oct 2020 Myriam Bontonou, Giulia Lioi, Nicolas Farrugia, Vincent Gripon

Few-shot learning addresses problems for which a limited number of training examples are available.

Few-Shot Learning

Some Remarks on Replicated Simulated Annealing

no code implementations30 Sep 2020 Vincent Gripon, Matthias Löwe, Franck Vermet

Recently authors have introduced the idea of training discrete weights neural networks using a mix between classical simulated annealing and a replica ansatz known from the statistical physics literature.

Gradients of Connectivity as Graph Fourier Bases of Brain Activity

no code implementations26 Sep 2020 Giulia Lioi, Vincent Gripon, Abdelbasset Brahim, François Rousseau, Nicolas Farrugia

The application of graph theory to model the complex structure and function of the brain has shed new light on its organization and function, prompting the emergence of network neuroscience.

GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning

1 code implementation4 Sep 2020 Lyes Khacef, Vincent Gripon, Benoit Miramond

In this work, we consider the problem of post-labeled few-shot unsupervised learning, a classification task where representations are learned in an unsupervised fashion, to be later labeled using very few annotated examples.

Classification Clustering +4

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.

Predicting the Accuracy of a Few-Shot Classifier

1 code implementation8 Jul 2020 Myriam Bontonou, Louis Béthune, Vincent Gripon

In the context of few-shot learning, one cannot measure the generalization ability of a trained classifier using validation sets, due to the small number of labeled samples.

Few-Shot Learning

Towards an Intrinsic Definition of Robustness for a Classifier

no code implementations9 Jun 2020 Théo Giraudon, Vincent Gripon, Matthias Löwe, Franck Vermet

The robustness of classifiers has become a question of paramount importance in the past few years.

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.


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.

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

Improved Visual Localization via Graph Smoothing

no code implementations7 Nov 2019 Carlos Lassance, Yasir Latif, Ravi Garg, Vincent Gripon, Ian Reid

One solution to this problem is to learn a deep neural network to infer the pose of a query image after learning on a dataset of images with known poses.

Image Retrieval Retrieval +1

Structural Robustness for Deep Learning Architectures

no code implementations11 Sep 2019 Carlos Lassance, Vincent Gripon, Jian Tang, Antonio Ortega

Deep Networks have been shown to provide state-of-the-art performance in many machine learning challenges.

BIG-bench Machine Learning

Comparing linear structure-based and data-driven latent spatial representations for sequence prediction

no code implementations19 Aug 2019 Myriam Bontonou, Carlos Lassance, Vincent Gripon, Nicolas Farrugia

Predicting the future of Graph-supported Time Series (GTS) is a key challenge in many domains, such as climate monitoring, finance or neuroimaging.

Time Series Time Series Analysis

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

A Unified Deep Learning Formalism For Processing Graph Signals

no code implementations1 May 2019 Myriam Bontonou, Carlos Lassance, Jean-Charles Vialatte, Vincent Gripon

Convolutional Neural Networks are very efficient at processing signals defined on a discrete Euclidean space (such as images).

Transfer Learning with Sparse Associative Memories

no code implementations4 Apr 2019 Quentin Jodelet, Vincent Gripon, Masafumi Hagiwara

In this paper, we introduce a novel layer designed to be used as the output of pre-trained neural networks in the context of classification.

General Classification Incremental Learning +1

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.


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

Laplacian Networks: Bounding Indicator Function Smoothness for Neural Network Robustness

no code implementations24 May 2018 Carlos Eduardo Rosar Kos Lassance, Vincent Gripon, Antonio Ortega

For the past few years, Deep Neural Network (DNN) robustness has become a question of paramount importance.

Matching Convolutional Neural Networks without Priors about Data

1 code implementation27 Feb 2018 Carlos Eduardo Rosar Kos Lassance, Jean-Charles Vialatte, Vincent Gripon

We propose an extension of Convolutional Neural Networks (CNNs) to graph-structured data, including strided convolutions and data augmentation on graphs.

Data Augmentation

Convolutional neural networks on irregular domains based on approximate vertex-domain translations

no code implementations27 Oct 2017 Bastien Pasdeloup, Vincent Gripon, Jean-Charles Vialatte, Dominique Pastor, Pascal Frossard

We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure.


Improving Accuracy of Nonparametric Transfer Learning via Vector Segmentation

no code implementations24 Oct 2017 Vincent Gripon, Ghouthi B. Hacene, Matthias Löwe, Franck Vermet

Transfer learning using deep neural networks as feature extractors has become increasingly popular over the past few years.

Transfer Learning

Robust Associative Memories Naturally Occuring From Recurrent Hebbian Networks Under Noise

no code implementations25 Sep 2017 Eliott Coyac, Vincent Gripon, Charlotte Langlais, Claude Berrou

In this paper, we are interested in demonstrating that those factors can actually lead to the appearance of robust associative memories.

Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs

no code implementations8 Jun 2017 Jean-Charles Vialatte, Vincent Gripon, Gilles Coppin

We propose a simple and generic layer formulation that extends the properties of convolutional layers to any domain that can be described by a graph.

Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction

no code implementations6 Mar 2017 Mathilde Ménoret, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon

Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals.

Dimensionality Reduction General Classification +1

Associative Memories to Accelerate Approximate Nearest Neighbor Search

no code implementations10 Nov 2016 Vincent Gripon, Matthias Löwe, Franck Vermet

In its canonical version, the complexity of the search is linear with both the dimension and the cardinal of the collection of vectors the search is performed in.

Quantization Retrieval

Generalizing the Convolution Operator to extend CNNs to Irregular Domains

no code implementations3 Jun 2016 Jean-Charles Vialatte, Vincent Gripon, Grégoire Mercier

Convolutional Neural Networks (CNNs) have become the state-of-the-art in supervised learning vision tasks.

Compression of Deep Neural Networks on the Fly

no code implementations29 Sep 2015 Guillaume Soulié, Vincent Gripon, Maëlys Robert

In this paper we introduce a novel compression method for deep neural networks that is performed during the learning phase.

Object Recognition Quantization

Memory vectors for similarity search in high-dimensional spaces

no code implementations10 Dec 2014 Ahmet Iscen, Teddy Furon, Vincent Gripon, Michael Rabbat, Hervé Jégou

We study an indexing architecture to store and search in a database of high-dimensional vectors from the perspective of statistical signal processing and decision theory.

Image Retrieval Vocal Bursts Intensity Prediction

Combating Corrupt Messages in Sparse Clustered Associative Memories

no code implementations27 Sep 2014 Zhe Yao, Vincent Gripon, Michael Rabbat

In this paper we analyze and extend the neural network based associative memory proposed by Gripon and Berrou.


Storing sequences in binary tournament-based neural networks

no code implementations1 Sep 2014 Xiaoran Jiang, Vincent Gripon, Claude Berrou, Michael Rabbat

An extension to a recently introduced architecture of clique-based neural networks is presented.


A study of retrieval algorithms of sparse messages in networks of neural cliques

no code implementations21 Aug 2013 Ala Aboudib, Vincent Gripon, Xiaoran Jiang

We introduce several families of algorithms to enhance the retrieval process performance in recently proposed sparse associative memories based on binary neural networks.


Storing non-uniformly distributed messages in networks of neural cliques

no code implementations24 Jul 2013 Bartosz Boguslawski, Vincent Gripon, Fabrice Seguin, Frédéric Heitzmann

Associative memories are data structures that allow retrieval of stored messages from part of their content.


A Massively Parallel Associative Memory Based on Sparse Neural Networks

no code implementations28 Mar 2013 Zhe Yao, Vincent Gripon, Michael G. Rabbat

In this paper we implement a variation of the Gripon-Berrou associative memory on a general purpose graphical processing unit (GPU).

Anomaly Detection Face Recognition +1

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