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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 771–780 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning	Aug 23, 2019	Meta-Learning	CodeCode Available	0	5
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks	Oct 31, 2019	Dialogue GenerationDiversity	CodeCode Available	0	5
Learning to Defer to a Population: A Meta-Learning Approach	Mar 5, 2024	Meta-LearningTraffic Sign Detection	CodeCode Available	0	5
Learning to Design RNA	Dec 31, 2018	CPUDeep Reinforcement Learning	CodeCode Available	0	5
Memory Efficient Neural Processes via Constant Memory Attention Block	May 23, 2023	Meta-Learning	CodeCode Available	0	5
Learning to Continually Learn Rapidly from Few and Noisy Data	Mar 6, 2021	Continual LearningMeta-Learning	CodeCode Available	0	5
Automated Privacy-Preserving Techniques via Meta-Learning	Jun 24, 2024	De-identificationMeta-Learning	CodeCode Available	0	5
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning	Dec 16, 2021	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
3FM: Multi-modal Meta-learning for Federated Tasks	Dec 15, 2023	Federated LearningMeta-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