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 28612870 of 3569 papers

TitleStatusHype
Learning to Propagate Labels: Transductive Propagation Network for Few-shot LearningCode0
Learning to Learn Cropping Models for Different Aspect Ratio RequirementsCode0
Learning to Rasterize DifferentiablyCode0
Learning to Learn By Self-CritiqueCode0
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-LearningCode0
Asynchronous Distributed Bilevel OptimizationCode0
Learning to Rectify for Robust Learning with Noisy LabelsCode0
Learning to reinforcement learnCode0
Provable Meta-Learning of Linear RepresentationsCode0
Learning to reinforcement learn for Neural Architecture SearchCode0
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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