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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 781–790 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Learning to Explore for Stochastic Gradient MCMC	Aug 17, 2024	image-classificationImage Classification	CodeCode Available	0	5
Automated Privacy-Preserving Techniques via Meta-Learning	Jun 24, 2024	De-identificationMeta-Learning	CodeCode Available	0	5
Discovering Weight Initializers with Meta Learning	May 20, 2021	Meta-Learning	CodeCode Available	0	5
Learning to Continually Learn Rapidly from Few and Noisy Data	Mar 6, 2021	Continual LearningMeta-Learning	CodeCode Available	0	5
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks	May 30, 2019	Bayesian InferenceMeta-Learning	CodeCode Available	0	5
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation	Nov 1, 2020	BIG-bench Machine LearningDomain Generalization	CodeCode Available	0	5
3FM: Multi-modal Meta-learning for Federated Tasks	Dec 15, 2023	Federated LearningMeta-Learning	CodeCode Available	0	5
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning	Dec 16, 2021	Meta-Learning	CodeCode Available	0	5
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning	May 27, 2019	Decision MakingDeep Reinforcement Learning	CodeCode Available	0	5
Learning to adapt: a meta-learning approach for speaker adaptation	Aug 30, 2018	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	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