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

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

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Repository for few-shot learning machine learning projects

[CVPR'17] Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to high performance at fast speed..

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)

Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)

A dataset of datasets for learning to learn from few examples

Collection for Few-shot Learning

Awesome Multitask Learning Resources

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

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

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

Code for the paper "Evolved Policy Gradients"

Python module to interface with OpenML

Tools for generating mini-ImageNet dataset and processing batches

A classified list of meta learning papers based on realm.

ProMP: Proximal Meta-Policy Search

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

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

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

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

My personal toolkit for PyTorch development.

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

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

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