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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 691–700 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Learning to Learn Cropping Models for Different Aspect Ratio Requirements	Jun 1, 2020	Image CroppingMeta-Learning	CodeCode Available	0	5
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning	Sep 24, 2022	Federated LearningMeta-Learning	CodeCode Available	0	5
Learning to Learn By Self-Critique	Dec 1, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
Learning to learn ecosystems from limited data -- a meta-learning approach	Oct 2, 2024	Meta-LearningPrediction	CodeCode Available	0	5
A MIND for Reasoning: Meta-learning for In-context Deduction	May 20, 2025	Meta-Learning	CodeCode Available	0	5
Black box meta-learning intrinsic rewards for sparse-reward environments	Jul 31, 2024	continuous-controlContinuous Control	CodeCode Available	0	5
Learning to Generate Noise for Multi-Attack Robustness	Jun 22, 2020	Meta-Learning	CodeCode Available	0	5
Learning to learn by gradient descent by gradient descent	Jun 14, 2016	Meta-Learning	CodeCode Available	0	5
BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity Representatio	Oct 15, 2023	Drug DiscoveryMeta-Learning	CodeCode Available	0	5
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms	Jan 30, 2019	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