45 citations · 100 across the 29 of their papers we have counts for
6 papers · 1 filter
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
Yassine El Ouahidi, Lucas Drumetz, Giulia Lioi +3
BCI Motor Imagery datasets usually are small and have different electrodes setups. When training a Deep Neural Network, one may want to capitalize on all these datasets to increase…
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings
Aymane Abdali, Vincent Gripon, Lucas Drumetz +1
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 restra…
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
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…
Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding
Yassine El Ouahidi, Hugo Tessier, Giulia Lioi +3
Graph Signal Processing is a promising framework to manipulate brain signals as it allows to encompass the spatial dependencies between the activity in regions of interest in the b…
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Yassir Bendou, Yuqing Hu, Raphael Lafargue +4
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 lab…
Preventing Manifold Intrusion with Locality: Local Mixup
Raphael Baena, Lucas Drumetz, Vincent Gripon
Mixup is a data-dependent regularization technique that consists in linearly interpolating input samples and associated outputs. It has been shown to improve accuracy when used to…