Few Shot Learning


Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of samples/images and very large datasets, few-shot learning aims to learn information about object categories from one, or only a few, training samples/images.

Overview

Advances in Few-Shot Learning: A Guided Tour
A deep dive into matching networks, prototypical networks and model-agnostic meta-learning.
few-shot-learning meta-learning tutorial article

Tutorials

One Shot Art
Implemented one shot learning approach to classify paintings according to artists.
few-shot-learning one-shot-learning art tutorial
Generalized Zero & Few-Shot Transfer for Facial Forgery Detection
Deep Distribution Transfer (DDT), a new transfer learning approach to address the problem of zero and few-shot transfer in the context of facial forgery.
zero-shot-learning few-shot-learning facial-forgery-detection fraud-detection

Libraries

Torchmeta
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
meta-learning few-shot-learning zero-shot-learning pytorch
Keras-FewShotLearning
Some State-of-the-Art few shot learning algorithms in tensorflow 2.
few-shot-learning keras tensorflow library
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