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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 1231–1240 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Clustering Indices based Automatic Classification Model Selection	May 23, 2023	ClassificationClustering	CodeCode Available	0	5
In-Context Learning through the Bayesian Prism	Jun 8, 2023	Bayesian InferenceIn-Context Learning	CodeCode Available	0	5
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery	Jan 25, 2024	Human Mesh RecoveryMeta-Learning	CodeCode Available	0	5
Leaping Through Time with Gradient-based Adaptation for Recommendation	Dec 11, 2021	Meta-LearningRecommendation Systems	CodeCode Available	0	5
Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect	Dec 6, 2023	Meta-Learning	CodeCode Available	0	5
Learning to Design RNA	Dec 31, 2018	CPUDeep Reinforcement Learning	CodeCode Available	0	5
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification	Aug 22, 2019	Few-Shot LearningFew-Shot Text Classification	CodeCode Available	0	5
Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative Embeddings	Oct 30, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
Generalizable Representation Learning for fMRI-based Neurological Disorder Identification	Dec 16, 2024	Meta-LearningRepresentation Learning	CodeCode Available	0	5
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation	Jun 14, 2023	Few-Shot LearningMemorization	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