Meta-Learning
1183 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 implementationsDatasets
TyDiQA
Meta-Dataset
M4
Omniglot
BlendedMVS
TyDiQA-GoldP
Kuzushiji-49
Most implemented papers
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
cbfinn/maml
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ICML 2017
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.
Prototypical Networks for Few-shot Learning
jakesnell/prototypical-networks
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NeurIPS 2017
We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.
Learning to Compare: Relation Network for Few-Shot Learning
floodsung/LearningToCompare_FSL
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CVPR 2018
Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.
On First-Order Meta-Learning Algorithms
This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i. e., learns quickly) when presented with a previously unseen task sampled from this distribution.
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
google-research/meta-dataset
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ICLR 2020
Few-shot classification refers to learning a classifier for new classes given only a few examples.
A Closer Look at Few-shot Classification
wyharveychen/CloserLookFewShot
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ICLR 2019
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
yinboc/few-shot-meta-baseline
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ICCV 2021
The edge between these two lines of works has yet been underexplored, and the effectiveness of meta-learning in few-shot learning remains unclear.
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial.
Meta-Learning for Semi-Supervised Few-Shot Classification
renmengye/few-shot-ssl-public
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ICLR 2018
To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes.
Learning to Reweight Examples for Robust Deep Learning
uber-research/learning-to-reweight-examples
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ICML 2018
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns.
