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 991–1000 of 3569 papers

TitleStatusHype
Dataset2Vec: Learning Dataset Meta-FeaturesCode0
Adaptive Cross-Modal Few-Shot LearningCode0
AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation BatteriesCode0
A Stylometric Inquiry into Hyperpartisan and Fake NewsCode0
Learning advisor networks for noisy image classificationCode0
Exploring the similarity of medical imaging classification problemsCode0
Exploring the Similarity of Representations in Model-Agnostic Meta-LearningCode0
Reevaluating Meta-Learning Optimization Algorithms Through Contextual Self-ModulationCode0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
Data-Driven Performance Guarantees for Classical and Learned OptimizersCode0
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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