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 29612970 of 3569 papers

TitleStatusHype
Learning One-Shot Imitation from Humans without HumansCode0
XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task CoverageCode0
MACFE: A Meta-learning and Causality Based Feature Engineering FrameworkCode0
Adversarial Monte Carlo Meta-Learning of Optimal Prediction ProceduresCode0
Experience-Based Evolutionary Algorithms for Expensive OptimizationCode0
Evolvability ES: Scalable and Direct Optimization of EvolvabilityCode0
MAD: Meta Adversarial Defense BenchmarkCode0
CMML: Contextual Modulation Meta Learning for Cold-Start RecommendationCode0
Magnification Generalization for Histopathology Image EmbeddingCode0
MIGS: Meta Image Generation from Scene GraphsCode0
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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