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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 451–460 of 3569 papers

Title	Date	Tasks	Status	Hype
Meta-Learning with Adaptive Hyperparameters	Oct 31, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	1
Meta-Learning with a Geometry-Adaptive Preconditioner	Apr 4, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Meta-learning with differentiable closed-form solvers	May 21, 2018	BIG-bench Machine LearningFew-Shot Learning	CodeCode Available	1
Meta-Learning with Differentiable Convex Optimization	Apr 7, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Meta-Learning with Self-Improving Momentum Target	Oct 11, 2022	Knowledge DistillationMeta-Learning	CodeCode Available	1
Meta-Learning with Sparse Experience Replay for Lifelong Language Learning	Sep 10, 2020	Continual LearningLifelong learning	CodeCode Available	1
Metalic: Meta-Learning In-Context with Protein Language Models	Oct 10, 2024	In-Context LearningMeta-Learning	CodeCode Available	1
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning	Sep 16, 2022	Contrastive LearningMeta-Learning	CodeCode Available	1
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation	Apr 21, 2022	DecoderFew-Shot Semantic Segmentation	CodeCode Available	1
Contrastive Meta Learning with Behavior Multiplicity for Recommendation	Feb 17, 2022	Contrastive LearningMeta-Learning	CodeCode Available	1

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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