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 2701–2710 of 3569 papers

TitleStatusHype
Meta-Learning for Planning: Automatic Synthesis of Sample Based Planners—0
Meta-learning for Positive-unlabeled Classification—0
Meta-Learning for Recalibration of EMG-Based Upper Limb Prostheses—0
Meta-Learning for Relative Density-Ratio Estimation—0
Meta-learning for RIS-assisted NOMA Networks—0
Meta-learning for robust child-adult classification from speech—0
Meta-learning for skin cancer detection using Deep Learning Techniques—0
Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments—0
Meta Learning for Task-Driven Video Summarization—0
Two-Step Meta-Learning for Time-Series Forecasting Ensemble—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