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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1571–1580 of 3569 papers

TitleStatusHype
Learning to learn to communicate—0
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning—0
Learning to Learn Transferable Generative Attack for Person Re-Identification—0
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning—0
Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks—0
Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor—0
Learning to Learn with Quantum Optimization via Quantum Neural Networks—0
Learning to Remember from a Multi-Task Teacher—0
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling—0
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified