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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 3551–3569 of 3569 papers

Title	Date	Tasks	Status	Hype
Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning	Jul 24, 2024	Language ModelingLanguage Modelling	CodeCode Available	0
Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks	Jan 29, 2021	Meta-LearningMeta Reinforcement Learning	CodeCode Available	0
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning	Oct 19, 2023	Bayesian OptimizationMeta-Learning	CodeCode Available	0
ProMP: Proximal Meta-Policy Search	Oct 16, 2018	Meta-LearningMeta Reinforcement Learning	CodeCode Available	0
AugFL: Augmenting Federated Learning with Pretrained Models	Mar 4, 2025	Federated LearningMeta-Learning	CodeCode Available	0
Adaptive Cross-Modal Few-Shot Learning	Feb 19, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0
Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events	Oct 3, 2021	Meta-LearningMeta Reinforcement Learning	CodeCode Available	0
Attentional Meta-learners for Few-shot Polythetic Classification	Jun 9, 2021	Classificationfeature selection	CodeCode Available	0
Sparse Gaussian Neural Processes	Apr 2, 2025	Gaussian ProcessesMeta-Learning	CodeCode Available	0
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning	Dec 8, 2020	Meta-LearningSemantic Segmentation	CodeCode Available	0
Meta Reinforcement Learning with Task Embedding and Shared Policy	May 16, 2019	Deep Reinforcement LearningMeta-Learning	CodeCode Available	0
Contextualizing Meta-Learning via Learning to Decompose	Jun 15, 2021	AttributeFew-Shot Image Classification	CodeCode Available	0
MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition	Nov 2, 2023	Meta-LearningRetrieval	CodeCode Available	0
The Effect of Diversity in Meta-Learning	Jan 27, 2022	DiversityFew-Shot Learning	CodeCode Available	0
Contextualizing Enhances Gradient Based Meta Learning	Jul 17, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	0
Sparsely Grouped Input Variables for Neural Networks	Nov 29, 2019	Meta-Learning	CodeCode Available	0
Contextual Gradient Scaling for Few-Shot Learning	Oct 20, 2021	Cross-Domain Few-ShotFew-Shot Learning	CodeCode Available	0
Fast Unsupervised Deep Outlier Model Selection with Hypernetworks	Jul 20, 2023	Meta-Learningmodel	CodeCode Available	0
Fast Meta-Learning for Adaptive Hierarchical Classifier Design	Nov 9, 2017	Binary ClassificationClassification	CodeCode Available	0

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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