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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 621–630 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
AReLU: Attention-based Rectified Linear Unit	Jun 24, 2020	Meta-LearningTransfer Learning	CodeCode Available	1	5
Exploiting Shared Representations for Personalized Federated Learning	Feb 14, 2021	Federated LearningMeta-Learning	CodeCode Available	1	5
Exploring Effective Factors for Improving Visual In-Context Learning	Apr 10, 2023	In-Context LearningMeta-Learning	CodeCode Available	1	5
Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning	Oct 8, 2021	Few-Shot LearningMeta-Learning	CodeCode Available	1	5
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning	Sep 16, 2022	Contrastive LearningMeta-Learning	CodeCode Available	1	5
Contrastive Meta-Learning for Partially Observable Few-Shot Learning	Jan 30, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	1	5
Contrastive Meta Learning with Behavior Multiplicity for Recommendation	Feb 17, 2022	Contrastive LearningMeta-Learning	CodeCode Available	1	5
Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn	May 12, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	1	5
ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification	May 16, 2023	ClassificationContrastive Learning	CodeCode Available	1	5
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning	Mar 25, 2019	AutoMLMeta-Learning	CodeCode Available	1	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