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 2561–2570 of 3569 papers

TitleStatusHype
Concept Discovery for Fast Adapatation—0
Conditional Meta-Learning of Linear Representations—0
Conditional Mutual Information-Based Generalization Bound for Meta Learning—0
Conditional Super Learner—0
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning—0
Confounder Identification-free Causal Visual Feature Learning—0
Confusable Learning for Large-class Few-Shot Classification—0
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning—0
Connection-Adaptive Meta-Learning—0
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification—0
Show:102550
← PrevPage 257 of 357Next →
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