Repository for few-shot learning machine learning projects

Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"

Implementation of Siamese Neural Networks for One-shot Image Recognition

This repo provides pytorch code which replicates the results of the Matching Networks for One Shot Learning paper on the Omniglot and MiniImageNet dataset

Multi-task learning for image classification implemented in PyTorch.

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

Cluttered Omniglot dataset and models

Implementation of Siamese-Networks for One Shot Learning in TensorFlow 2.0

a deep recurrent model for exchangeable data

Implementation of Matching Networks for One Shot Learning in TensorFlow 2.0

Example of one shot learning and few shot learning with omniglot dataset.

This repository implements the paper, Model-Agnostic Meta-Leanring for Fast Adaptation of Deep Networks.

Implementation of Siamese Networks using PyTorch

Implementation of Few-shot Binary Image Classification using Contrastive Learning-based Approach in PyTorch

Implementation for paper "BATMANN: A Binarized-All-Through Memory-Augmented Neural Network for Efficient In-Memory Computing"

Prototypical Networks for the task of few-shot image classification on Omniglot and mini-ImageNet.

Implementation of Variational Memory Addressing for generative few-shot learning in PyTorch

Implementation of "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

Memory Augmented Neural Networks, a Black-Box Meta-Learner that uses an LSTMs for few-shot classification

Reimplementation of "VAE with a VampPrior" by Jakub M. Tomczak et al., as part of the DD2434 Machine Learning, Advanced Course at KTH