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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 801–810 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Learning to Learn Transferable Attack	Dec 10, 2021	Adversarial AttackData Augmentation	CodeCode Available	0	5
Diverse Few-Shot Text Classification with Multiple Metrics	May 19, 2018	ClassificationFew-Shot Learning	CodeCode Available	0	5
AutoLoss: Learning Discrete Schedules for Alternate Optimization	Oct 4, 2018	Image GenerationMachine Translation	CodeCode Available	0	5
Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations	Apr 1, 2022	Domain AdaptationMeta-Learning	CodeCode Available	0	5
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning	Dec 15, 2023	Contrastive LearningMeta-Learning	CodeCode Available	0	5
Diversity with Cooperation: Ensemble Methods for Few-Shot Classification	Mar 27, 2019	ClassificationDiversity	CodeCode Available	0	5
Learning to adapt: a meta-learning approach for speaker adaptation	Aug 30, 2018	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0	5
Learning to Learn Single Domain Generalization	Mar 30, 2020	Domain GeneralizationMeta-Learning	CodeCode Available	0	5
META-Learning Eligibility Traces for More Sample Efficient Temporal Difference Learning	Jun 16, 2020	Meta-Learning	CodeCode Available	0	5
Learning Low-Dimensional Embeddings for Black-Box Optimization	May 2, 2025	Meta-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