SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2026–2050 of 3569 papers

TitleStatusHype
Efficient Federated Meta-Learning over Multi-Access Wireless Networks—0
Learning to Segment Medical Images from Few-Shot Sparse LabelsCode0
Prototype Completion for Few-Shot LearningCode1
Known Operator Learning and Hybrid Machine Learning in Medical Imaging --- A Review of the Past, the Present, and the Future—0
The Role of Global Labels in Few-Shot Classification and How to Infer Them—0
Meta Gradient Adversarial AttackCode1
Generating Personalized Dialogue via Multi-Task Meta-Learning—0
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight TransformerCode1
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning—0
Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach—0
Multimodal Meta-Learning for Time Series Regression—0
Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks—0
Dynamic Relevance Learning for Few-Shot Object DetectionCode1
Bayesian Active Meta-Learning for Few Pilot Demodulation and EqualizationCode0
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learningCode0
Bayesian Model-Agnostic Meta-Learning with Matrix-Valued Kernels for Quality Estimation—0
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages—0
Meta-Learning for Few-Shot Named Entity Recognition—0
Multi-Pair Text Style Transfer for Unbalanced Data via Task-Adaptive Meta-Learning—0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification—0
Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional CategoriesCode0
Continual Quality Estimation with Online Bayesian Meta-Learning—0
EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles—0
ProtAugment: Intent Detection Meta-Learning through Unsupervised Diverse ParaphrasingCode1
Meta Learning and Its Applications to Natural Language Processing—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified