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 351360 of 3569 papers

TitleStatusHype
Rapid Neural Architecture Search by Learning to Generate Graphs from DatasetsCode1
Few-Shot Learning with a Strong TeacherCode1
How to Train Your MAML to Excel in Few-Shot ClassificationCode1
Relational VAE: A Continuous Latent Variable Model for Graph Structured DataCode1
MAML is a Noisy Contrastive Learner in ClassificationCode1
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image SegmentationCode1
Mutual-Information Based Few-Shot ClassificationCode1
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth ImagesCode1
Transfer Bayesian Meta-learning via Weighted Free Energy MinimizationCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
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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