A PyTorch Library for Meta-learning Research

Repository for few-shot learning machine learning projects

Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch

A PyTorch implementation of Model Agnostic Meta-Learning (MAML) that faithfully reproduces the results from the original paper.

Source code for NeurIPS 2020 paper "Meta-Learning with Adaptive Hyperparameters"

[CVPR2021] Meta Batch-Instance Normalization for Generalizable Person Re-Identification

Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)

A collection of Gradient-Based Meta-Learning Algorithms with pytorch

NAACL '24 (Best Demo Paper RunnerUp) / MlSys @ NeurIPS '23 - RedCoast: A Lightweight Tool to Automate Distributed Training and Inference

This repository contains the implementation for the paper - Exploration via Hierarchical Meta Reinforcement Learning.

Meta-learning model agnostic (MAML) implementation for cross-accented ASR

PyTorch implementation of "How to Train Your MAML to Excel in Few-Shot Classification"

Meta-Learning for EEG, Sleep Staging, Transfer Learning, Pre-trained EEG, PSG datasets (IEEE Journal of Biomedical and Health Informatics)

Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning"

Code for meta-learning initializations for image segmentation

Implementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in TensorFlow 2.

Model Agnostic Meta Learning (MAML) implemented in Flax, the neural network library for JAX.

MAML and Reptile sine wave regression example in PyTorch

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

Implementation of MAML in numpy, deriving gradients and implementing backprop manually