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 1821–1830 of 3569 papers

TitleStatusHype
Reservoir Computing for Fast, Simplified Reinforcement Learning on Memory Tasks—0
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning—0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning—0
Rethinking Class Imbalance in Machine Learning—0
Rethinking Meta-Learning from a Learning Lens—0
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning—0
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks—0
Retrieval-Augmented Meta Learning for Low-Resource Text Classification—0
Revealing the Incentive to Cause Distributional Shift—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