Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning

FSL-Mate: A collection of resources for few-shot learning (FSL).

A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.

Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow

A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

Implementation of Siamese Neural Networks for One-shot Image Recognition

Tools for generating mini-ImageNet dataset and processing batches

Implementation of One-Shot Object Detection with Co-Attention and Co-Excitation in Pytorch

Siamese Mask R-CNN model for one-shot instance segmentation

OneShot Learning-based hotword detection.

Implementation of Siamese Networks for image one-shot learning by PyTorch, train and test model on dataset Omniglot

One Shot Learning using Memory-Augmented Neural Networks (MANN) based on Neural Turing Machine architecture in Tensorflow

Matching Networks for one shot learning

Some State-of-the-Art few shot learning algorithms in tensorflow 2

Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

TensorFlow implementation of Neural Turing Machines (NTM), with its application on one-shot learning (MANN)

Deep Learning - one shot learning for speaker recognition using Filter Banks

Implementation of One Shot Learning using Convolutional Siamese Networks on Omniglot Dataset

Implementation of Facial Recognition System Using Facenet based on One Shot Learning Using Siamese Networks

Tensorflow implementation of NIPS 2017 Paper "Prototypical Networks for Few-shot Learning"

One-Shot Learning with Triplet CNNs in Pytorch

Papers and code related to 'Less Than One'-Shot (LO-Shot) Learning

Find logos in images and videos in just one-shot. Never be embarrassed again to say that you have a small data situation!

One-shot Siamese Neural Network, using TensorFlow 2.0, based on the work presented by Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. we used the “Labeled Faces in the Wild” dataset with over 5,700 different people. Some people have a single image, while others have dozens.

A Deep One-Shot Network for Query-based Logo Retrieval [Pattern Recognition 2019, Elsevier]

Implementation of Prototypical Networks for Few-shot Learning in TensorFlow 2.0

awesome few shot / meta learning papers

Identifying forged signatures using convolutional siamese networks implemented in Keras

Codes for "Property-Aware Relation Networks for Few-shot Molecular Property Prediction (NeurIPS 2021)".

Cluttered Omniglot dataset and models