8 citations · 14 across the 11 of their papers we have counts for
4 papers · 1 filter
A Statistical Model for Predicting Generalization in Few-Shot Classification
Yassir Bendou, Vincent Gripon, Bastien Pasdeloup +5
The estimation of the generalization error of classifiers often relies on a validation set. Such a set is hardly available in few-shot learning scenarios, a highly disregarded shor…
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…
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…