no code implementations • 31 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.
no code implementations • 15 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.
no code implementations • 29 Jan 2024 • Raphael Lafargue, Yassir Bendou, Bastien Pasdeloup, Jean-Philippe Diguet, Ian Reid, Vincent Gripon, Jack Valmadre
Fine-tuning is ineffective for few-shot learning, since the target dataset contains only a handful of examples.
1 code implementation • 24 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.
1 code implementation • 11 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.
1 code implementation • 16 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.
1 code implementation • 13 Dec 2022 • Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Lukas Mauch, Stefan Uhlich, Fabien Cardinaux, Ghouthi Boukli Hacene, Javier Alonso Garcia
Such a set is hardly available in few-shot learning scenarios, a highly disregarded shortcoming in the field.
no code implementations • 28 Oct 2022 • Yassine El Ouahidi, Lucas Drumetz, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon
BCI Motor Imagery datasets usually are small and have different electrodes setups.
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.
2 code implementations • 24 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.
Ranked #1 on Few-Shot Learning on Mini-Imagenet 5-way (1-shot)
no code implementations • 8 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.
no code implementations • 12 Jan 2021 • Mounia Hamidouche, Carlos Lassance, Yuqing Hu, Lucas Drumetz, Bastien Pasdeloup, Vincent Gripon
In machine learning, classifiers are typically susceptible to noise in the training data.
no code implementations • 30 Jun 2020 • Yassine El Ouahidi, Matis Feller, Matthieu Talagas, Bastien Pasdeloup
In this paper, we introduce a novel and interpretable methodology to cluster subjects suffering from cancer, based on features extracted from their biopsies.
no code implementations • 27 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.
no code implementations • 6 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.