Meta-Learning

1169 papers with code • 4 benchmarks • 19 datasets

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )

Libraries

Use these libraries to find Meta-Learning models and implementations

Most implemented papers

How to train your MAML

AntreasAntoniou/HowToTrainYourMAMLPytorch ICLR 2019

The field of few-shot learning has recently seen substantial advancements.

Meta Pseudo Labels

google-research/google-research CVPR 2021

We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90. 2% on ImageNet, which is 1. 6% better than the existing state-of-the-art.

Learning to learn by gradient descent by gradient descent

deepmind/learning-to-learn NeurIPS 2016

The move from hand-designed features to learned features in machine learning has been wildly successful.

Learning to reinforcement learn

awjuliani/Meta-RL 17 Nov 2016

We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

rlworkgroup/metaworld 24 Oct 2019

Therefore, if the aim of these methods is to enable faster acquisition of entirely new behaviors, we must evaluate them on task distributions that are sufficiently broad to enable generalization to new behaviors.

LEAF: A Benchmark for Federated Settings

TalwalkarLab/leaf 3 Dec 2018

Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day.

Meta-Learning with Differentiable Convex Optimization

kjunelee/MetaOptNet CVPR 2019

We propose to use these predictors as base learners to learn representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition benchmarks.

ProMP: Proximal Meta-Policy Search

jonasrothfuss/promp ICLR 2019

Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

vincent-thevenin/Realistic-Neural-Talking-Head-Models ICCV 2019

In order to create a personalized talking head model, these works require training on a large dataset of images of a single person.

Meta-Learning Representations for Continual Learning

Khurramjaved96/mrcl NeurIPS 2019

We show that it is possible to learn naturally sparse representations that are more effective for online updating.