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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 481–490 of 3569 papers

Title	Date	Tasks	Status	Hype
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks	Sep 17, 2020	General ClassificationLanguage Modeling	CodeCode Available	1
Few-Shot Unsupervised Continual Learning through Meta-Examples	Sep 17, 2020	ClusteringContinual Learning	CodeCode Available	1
Few-shot Learning with LSSVM Base Learner and Transductive Modules	Sep 12, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	1
Meta Learning for Few-Shot One-class Classification	Sep 11, 2020	ClassificationGeneral Classification	CodeCode Available	1
Meta-Learning with Sparse Experience Replay for Lifelong Language Learning	Sep 10, 2020	Continual LearningLifelong learning	CodeCode Available	1
Transfer Graph Neural Networks for Pandemic Forecasting	Sep 10, 2020	Meta-LearningRepresentation Learning	CodeCode Available	1
Prototype Completion with Primitive Knowledge for Few-Shot Learning	Sep 10, 2020	AttributeFew-Shot Learning	CodeCode Available	1
Grounded Language Learning Fast and Slow	Sep 3, 2020	Grounded language learningMeta-Learning	CodeCode Available	1
Simulating Unknown Target Models for Query-Efficient Black-box Attacks	Sep 2, 2020	Knowledge DistillationMeta-Learning	CodeCode Available	1
Transductive Information Maximization For Few-Shot Learning	Aug 25, 2020	Few-Shot Image ClassificationFew-Shot 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