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 41–50 of 3569 papers

TitleStatusHype
Global Convergence and Generalization Bound of Gradient-Based Meta-Learning with Deep Neural NetsCode2
Gödel Agent: A Self-Referential Agent Framework for Recursive Self-ImprovementCode2
Learning Universal PredictorsCode2
Learning Deep Time-index Models for Time Series ForecastingCode2
Frustratingly Simple Few-Shot Object DetectionCode2
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation ExploitationCode2
MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-LearningCode2
NAVIX: Scaling MiniGrid Environments with JAXCode2
A Practitioner's Guide to Continual Multimodal PretrainingCode2
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object DetectionCode1
Show:102550
← PrevPage 5 of 357Next →
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