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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 1111–1120 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation	May 30, 2023	Data AugmentationImage Segmentation	CodeCode Available	0	5
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning	Dec 10, 2024	Evolutionary AlgorithmsLifelong learning	CodeCode Available	0	5
Joint inference and input optimization in equilibrium networks	Nov 25, 2021	DenoisingMeta-Learning	CodeCode Available	0	5
Learning to Self-Train for Semi-Supervised Few-Shot Classification	Jun 3, 2019	ClassificationGeneral Classification	CodeCode Available	0	5
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation	Sep 30, 2023	Density EstimationMeta-Learning	CodeCode Available	0	5
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?	Mar 6, 2022	Meta-Learningmodel	CodeCode Available	0	5
Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures	Feb 26, 2020	Meta-Learning	CodeCode Available	0	5
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model Compression	Oct 1, 2022	Knowledge DistillationLanguage Modeling	CodeCode Available	0	5
Concurrent Meta Reinforcement Learning	Mar 7, 2019	Efficient ExplorationMeta-Learning	CodeCode Available	0	5
Concept-free Causal Disentanglement with Variational Graph Auto-Encoder	Nov 17, 2023	DisentanglementMeta-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