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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 721–730 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module	Feb 7, 2019	DecoderFew-Shot Learning	CodeCode Available	0	5
Learning to Generate Noise for Multi-Attack Robustness	Jun 22, 2020	Meta-Learning	CodeCode Available	0	5
Been There, Done That: Meta-Learning with Episodic Recall	May 24, 2018	Meta-Learning	CodeCode Available	0	5
BayesPCN: A Continually Learnable Predictive Coding Associative Memory	May 20, 2022	BIG-bench Machine LearningMeta-Learning	CodeCode Available	0	5
Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual Learning	Apr 8, 2022	Computational EfficiencyContinual Learning	CodeCode Available	0	5
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks	Nov 10, 2019	DiversityEntity Typing	CodeCode Available	0	5
Addressing Catastrophic Forgetting in Few-Shot Problems	Apr 30, 2020	ClassificationGeneral Classification	CodeCode Available	0	5
Learning to Explore for Stochastic Gradient MCMC	Aug 17, 2024	image-classificationImage Classification	CodeCode Available	0	5
Learning to Forget for Meta-Learning	Jun 13, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning	May 27, 2019	Decision MakingDeep Reinforcement 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