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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 921–930 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Learning Fast Adaptation with Meta Strategy Optimization	Sep 28, 2019	Meta-Learning	CodeCode Available	0	5
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction	Jan 24, 2019	Meta-LearningPrediction	CodeCode Available	0	5
Learning Deep Morphological Networks with Neural Architecture Search	Jun 14, 2021	Edge DetectionMeta-Learning	CodeCode Available	0	5
Attentional Meta-learners for Few-shot Polythetic Classification	Jun 9, 2021	Classificationfeature selection	CodeCode Available	0	5
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks	Jul 6, 2021	BIG-bench Machine LearningDomain Generalization	CodeCode Available	0	5
Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation	Jul 7, 2022	Abstractive Text SummarizationLanguage Modeling	CodeCode Available	0	5
Image Deformation Meta-Networks for One-Shot Learning	May 28, 2019	Few-Shot Image ClassificationMeta-Learning	CodeCode Available	0	5
Learning How to Demodulate from Few Pilots via Meta-Learning	Mar 6, 2019	Meta-Learning	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
Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective	Aug 28, 2019	Deep LearningLearning Theory	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