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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 811–820 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Learning to Continually Learn Rapidly from Few and Noisy Data	Mar 6, 2021	Continual LearningMeta-Learning	CodeCode Available	0	5
Learning to Defer to a Population: A Meta-Learning Approach	Mar 5, 2024	Meta-LearningTraffic Sign Detection	CodeCode Available	0	5
AutoLoss: Learning Discrete Schedules for Alternate Optimization	Oct 4, 2018	Image GenerationMachine Translation	CodeCode Available	0	5
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning	Dec 15, 2023	Contrastive LearningMeta-Learning	CodeCode Available	0	5
Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning	Aug 23, 2019	Meta-Learning	CodeCode Available	0	5
MetaAge: Meta-Learning Personalized Age Estimators	Jul 12, 2022	Age EstimationMeta-Learning	CodeCode Available	0	5
An Investigation of Few-Shot Learning in Spoken Term Classification	Dec 26, 2018	Few-Shot LearningGeneral Classification	CodeCode Available	0	5
AutoML for Multi-Class Anomaly Compensation of Sensor Drift	Feb 26, 2025	AutoMLEnsemble Learning	CodeCode Available	0	5
Learning One-Shot Imitation from Humans without Humans	Nov 4, 2019	Imitation LearningMeta-Learning	CodeCode Available	0	5
Learning Task-Aware Energy Disaggregation: a Federated Approach	Apr 14, 2022	Federated LearningMeta-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