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 851–860 of 3569 papers

TitleStatusHype
Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization—0
Unleash Model Potential: Bootstrapped Meta Self-supervised Learning—0
MetaWeather: Few-Shot Weather-Degraded Image RestorationCode1
FAM: fast adaptive federated meta-learning—0
The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning—0
MetaGCD: Learning to Continually Learn in Generalized Category DiscoveryCode1
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR—0
Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary citiesCode0
HyperLoRA for PDEs—0
Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain GeneralizationCode1
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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