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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 1121–1130 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Comprehensive Fair Meta-learned Recommender System	Jun 9, 2022	counterfactualFairness	CodeCode Available	0	5
Adaptation-Agnostic Meta-Training	Aug 24, 2021	Meta-Learning	CodeCode Available	0	5
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation	May 30, 2023	Data AugmentationImage Segmentation	CodeCode Available	0	5
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model Compression	Oct 1, 2022	Knowledge DistillationLanguage Modeling	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
Learning How to Demodulate from Few Pilots via Meta-Learning	Mar 6, 2019	Meta-Learning	CodeCode Available	0	5
Learning to Generate Noise for Multi-Attack Robustness	Jun 22, 2020	Meta-Learning	CodeCode Available	0	5
A Partially Supervised Reinforcement Learning Framework for Visual Active Search	Oct 15, 2023	Deep Reinforcement LearningMeta-Learning	CodeCode Available	0	5
Inverse Learning with Extremely Sparse Feedback for Recommendation	Nov 14, 2023	Meta-Learning	CodeCode Available	0	5
Component-Based Fairness in Face Attribute Classification with Bayesian Network-informed Meta Learning	May 3, 2025	AttributeFace Recognition	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