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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 1311–1320 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models	Nov 10, 2023	In-Context LearningMeta-Learning	CodeCode Available	0	5
Improving Meta-Learning Generalization with Activation-Based Early-Stopping	Aug 3, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
In-Context Learning through the Bayesian Prism	Jun 8, 2023	Bayesian InferenceIn-Context Learning	CodeCode Available	0	5
Feature Extractor Stacking for Cross-domain Few-shot Learning	May 12, 2022	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	0	5
Improving Memory Efficiency for Training KANs via Meta Learning	Jun 9, 2025	image-classificationImage Classification	CodeCode Available	0	5
Few-Shot Learning with Localization in Realistic Settings	Apr 9, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
Improving Meta-Continual Learning Representations with Representation Replay	Sep 29, 2021	Continual LearningMeta-Learning	CodeCode Available	0	5
Hacking Task Confounder in Meta-Learning	Dec 10, 2023	Meta-LearningTransfer Learning	CodeCode Available	0	5
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery	Jan 25, 2024	Human Mesh RecoveryMeta-Learning	CodeCode Available	0	5
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation	Sep 30, 2023	Density EstimationMeta-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