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 1551–1560 of 3569 papers

TitleStatusHype
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
Persian Natural Language Inference: A Meta-learning approachCode0
Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling—0
Meta-Learning Sparse Compression Networks—0
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks—0
Neural-Fly Enables Rapid Learning for Agile Flight in Strong WindsCode2
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities—0
Meta Balanced Network for Fair Face Recognition—0
Warm-starting DARTS using meta-learning—0
Multi-Environment Meta-Learning in Stochastic Linear Bandits—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