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 531540 of 3569 papers

TitleStatusHype
Graph Prototypical Networks for Few-shot Learning on Attributed NetworksCode1
Learning with AMIGo: Adversarially Motivated Intrinsic GoalsCode1
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian ProcessesCode1
Self-Supervised Prototypical Transfer Learning for Few-Shot ClassificationCode1
Self-supervised Knowledge Distillation for Few-shot LearningCode1
MetaSDF: Meta-learning Signed Distance FunctionsCode1
Personalized Federated Learning with Moreau EnvelopesCode1
Flexible Dataset Distillation: Learn Labels Instead of ImagesCode1
Graph Meta Learning via Local SubgraphsCode1
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and ArchitecturesCode1
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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