SOTAVerified|Agents Browse Leaderboard About

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 891–900 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Learning to Defer to a Population: A Meta-Learning Approach	Mar 5, 2024	Meta-LearningTraffic Sign Detection	CodeCode Available	0	5
Learning to learn by gradient descent by gradient descent	Jun 14, 2016	Meta-Learning	CodeCode Available	0	5
Meta-Learning Bidirectional Update Rules	Apr 10, 2021	Meta-Learning	CodeCode Available	0	5
Learning Invariances for Policy Generalization	Sep 7, 2018	BIG-bench Machine LearningData Augmentation	CodeCode Available	0	5
Algorithm Selection on a Meta Level	Jul 20, 2021	Ensemble LearningMeta-Learning	CodeCode Available	0	5
Learning Low-Dimensional Embeddings for Black-Box Optimization	May 2, 2025	Meta-Learning	CodeCode Available	0	5
Learning How to Demodulate from Few Pilots via Meta-Learning	Mar 6, 2019	Meta-Learning	CodeCode Available	0	5
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction	Jan 24, 2019	Meta-LearningPrediction	CodeCode Available	0	5
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module	Feb 7, 2019	DecoderFew-Shot Learning	CodeCode Available	0	5
Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization	Feb 1, 2023	Generalized Zero-Shot LearningMeta-Learning	CodeCode Available	0	5

Show:10 25 50

← PrevPage 90 of 357Next →

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