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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 971–980 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification	May 7, 2021	Generalizable Person Re-identificationMeta-Learning	CodeCode Available	0	5
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks	Jul 6, 2021	BIG-bench Machine LearningDomain Generalization	CodeCode Available	0	5
Boosting Lightweight Single Image Super-resolution via Joint-distillation	Oct 17, 2021	Image Super-ResolutionMeta-Learning	CodeCode Available	0	5
Exploiting Adapters for Cross-lingual Low-resource Speech Recognition	May 18, 2021	Cross-Lingual ASRGeneral Knowledge	CodeCode Available	0	5
Leaping Through Time with Gradient-based Adaptation for Recommendation	Dec 11, 2021	Meta-LearningRecommendation Systems	CodeCode Available	0	5
Data Valuation using Reinforcement Learning	Sep 25, 2019	Data ValuationDomain Adaptation	CodeCode Available	0	5
Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction	Apr 8, 2025	Link PredictionMeta-Learning	CodeCode Available	0	5
learn2learn: A Library for Meta-Learning Research	Aug 27, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning	Apr 17, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
Dataset Distillation with Infinitely Wide Convolutional Networks	Jul 27, 2021	Dataset Distillationimage-classification	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