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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 1341–1350 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging	Jun 3, 2022	Medical Image AnalysisMeta-Learning	CodeCode Available	0	5
Improving Memory Efficiency for Training KANs via Meta Learning	Jun 9, 2025	image-classificationImage Classification	CodeCode Available	0	5
Improving Federated Learning Personalization via Model Agnostic Meta Learning	Sep 27, 2019	Federated LearningMeta-Learning	CodeCode Available	0	5
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification	Aug 22, 2019	Few-Shot LearningFew-Shot Text Classification	CodeCode Available	0	5
Improving Meta-Continual Learning Representations with Representation Replay	Sep 29, 2021	Continual LearningMeta-Learning	CodeCode Available	0	5
Improving Both Domain Robustness and Domain Adaptability in Machine Translation	Dec 15, 2021	Domain AdaptationMachine Translation	CodeCode Available	0	5
Fast Meta-Learning for Adaptive Hierarchical Classifier Design	Nov 9, 2017	Binary ClassificationClassification	CodeCode Available	0	5
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks	Oct 14, 2024	AllFew-Shot Text Classification	CodeCode Available	0	5
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function	Oct 4, 2024	ClassificationContrastive 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

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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