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

Showing 301310 of 3569 papers

TitleStatusHype
Domain Adaptive Few-Shot Open-Set LearningCode1
ARCADe: A Rapid Continual Anomaly DetectorCode1
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the WildCode1
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-LearningCode1
DPGN: Distribution Propagation Graph Network for Few-shot LearningCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Dynamic Relevance Learning for Few-Shot Object DetectionCode1
Editing Factual Knowledge in Language ModelsCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified