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Found 6 papers, 4 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)
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples.
Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network
After discussion, it is found that data-driven models integrated engineering or physical knowledge can significantly improve the urban building energy simulation.
Improving Classification Accuracy with Graph Filtering
In machine learning, classifiers are typically susceptible to noise in the training data.
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples.
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification
In few-shot classification, the aim is to learn models able to discriminate classes using only a small number of labeled examples.
