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Found 15 papers, 5 papers with code
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
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)
A Strong and Simple Deep Learning Baseline for BCI MI Decoding
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI.
A Statistical Model for Predicting Generalization in Few-Shot Classification
Such a set is hardly available in few-shot learning scenarios, a highly disregarded shortcoming in the field.
Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning
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.
Convolutional neural networks on irregular domains based on approximate vertex-domain translations
We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure.
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction
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.
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies
In this paper, we introduce a novel and interpretable methodology to cluster subjects suffering from cancer, based on features extracted from their biopsies.
Improving Classification Accuracy with Graph Filtering
In machine learning, classifiers are typically susceptible to noise in the training data.
Graphs as Tools to Improve Deep Learning Methods
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.
Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding
In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals.
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
BCI Motor Imagery datasets usually are small and have different electrodes setups.
Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach
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.
Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes
Fine-tuning is ineffective for few-shot learning, since the target dataset contains only a handful of examples.
Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding
In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision.
LLM meets Vision-Language Models for Zero-Shot One-Class Classification
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.
