104 Open source projects that are alternatives of or similar to Mfe

TensorFlow 2.0 implementation of MAML.

LibFewShot: A Comprehensive Library for Few-shot Learning.

Implementation of our paper "Meta Reinforcement Learning with Task Embedding and Shared Policy"

Code for "One-Shot Visual Imitation Learning via Meta-Learning"

Great resources for making Reinforcement Learning work in Real Life situations. Papers,projects and more.

The code repository for "Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions"

Tools for generating mini-ImageNet dataset and processing batches

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

Code for the paper "Evolved Policy Gradients"

Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight)

Python module to interface with OpenML

A classified list of meta learning papers based on realm.

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

ProMP: Proximal Meta-Policy Search

Meta-Transfer Learning for Zero-Shot Super-Resolution (CVPR, 2020)

A list of recent papers about Meta / few-shot learning methods applied in NLP areas.

NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).

All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.

Learning to Learn how to Learn: Self-Adaptive Visual Navigation using Meta-Learning (https://arxiv.org/abs/1812.00971)

The code for paper "CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning"

Learning Meta Face Recognition in Unseen Domains, CVPR, Oral, 2020

My personal toolkit for PyTorch development.

pytorch implementation of Optimization as a Model for Few-shot Learning

Meta R-CNN : Towards General Solver for Instance-level Low-shot Learning

PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models (IN PROGRESS)

Bilevel Optimization Library in Python for Multi-Task and Meta Learning

The source codes of the paper "Improving Few-shot Text Classification via Pretrained Language Representations" and "When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification".

A modular toolbox for meta-learning research with a focus on speed and reproducibility.

Paper Lists, Notes and Slides, Focus on NLP. For summarization, please refer to https://github.com/xcfcode/Summarization-Papers

The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].

Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".

[ICLR'19] Meta-learning with differentiable closed-form solvers

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

A PyTorch Library for Meta-learning Research

Memory efficient MAML using gradient checkpointing

Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

Meta learning with BERT as a learner

Graph meta learning via local subgraphs (NeurIPS 2020)

Combine multiple MNIST digits to create datasets with 100/1000 classes for few-shot learning/meta-learning

Codes and datasets for AAAI-2021 paper "Learning to Pre-train Graph Neural Networks"

Tensorflow Implementation of MAML

"Learning to learn by gradient descent by gradient descent "by PyTorch -- a simple re-implementation.

Few-shot binary text classification with Induction Networks and Word2Vec weights initialization

Code accompanying the ICML-2018 paper "Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace"