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 631640 of 3569 papers

TitleStatusHype
Meta-Transfer Learning for Few-Shot LearningCode1
Few-shot Object Detection via Feature ReweightingCode1
LEAF: A Benchmark for Federated SettingsCode1
Transferring Knowledge across Learning ProcessesCode1
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-LearningCode1
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing InterferenceCode1
How to train your MAMLCode1
Fast Context Adaptation via Meta-LearningCode1
Learning Quickly to Plan Quickly Using Modular Meta-LearningCode1
Characterizing classification datasets: a study of meta-features for meta-learningCode1
Show:102550
← PrevPage 64 of 357Next →

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