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Meta-Learning

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 )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 661–670 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in Recommendation	Jan 24, 2024	Meta-Learning	CodeCode Available	0	5
Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization	Aug 2, 2021	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
Learning to Learn Variational Semantic Memory	Oct 20, 2020	Few-Shot LearningGeneral Knowledge	CodeCode Available	0	5
Learning to Multi-Task by Active Sampling	Feb 20, 2017	Active LearningMeta-Learning	CodeCode Available	0	5
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning	Jan 1, 2021	Domain AdaptationMeta-Learning	CodeCode Available	0	5
Learning to Learn from Noisy Labeled Data	Dec 13, 2018	image-classificationImage Classification	CodeCode Available	0	5
Learning to Learn Cropping Models for Different Aspect Ratio Requirements	Jun 1, 2020	Image CroppingMeta-Learning	CodeCode Available	0	5
Learning to learn ecosystems from limited data -- a meta-learning approach	Oct 2, 2024	Meta-LearningPrediction	CodeCode Available	0	5
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset	Apr 2, 2019	General ClassificationMeta-Learning	CodeCode Available	0	5
Learning to learn by gradient descent by gradient descent	Jun 14, 2016	Meta-Learning	CodeCode Available	0	5

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All datasets ML10 MT50 ML45 OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MZ+Recon	Meta-train success rate	97.8	—	Unverified
2	MZ	Meta-train success rate	97.6	—	Unverified
3	MAML	Meta-test success rate	36	—	Unverified
4	RL^2	Meta-test success rate	10	—	Unverified
5	DnC	Meta-test success rate	5.4	—	Unverified
6	PEARL	Meta-test success rate	0	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SoftModule	Average Success Rate	60	—	Unverified
2	Multi-task multi-head SAC	Average Success Rate	35.85	—	Unverified
3	DisCor	Average Success Rate	26	—	Unverified
4	NDP	Average Success Rate	11	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MZ+Recon	Meta-test success rate (zero-shot)	18.5	—	Unverified
2	MZ	Meta-test success rate (zero-shot)	17.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Metadrop	% Test Accuracy	95.75	—	Unverified