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 3421–3430 of 3569 papers

TitleStatusHype
Meta-Learning Approaches for a One-Shot Collective-Decision Aggregation: Correctly Choosing how to Choose Correctly—0
Meta-learning approaches for few-shot learning: A survey of recent advances—0
Meta-Learning Approaches for Improving Detection of Unseen Speech Deepfakes—0
Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models—0
Meta Learning as Bayes Risk Minimization—0
Meta-learning as Learning the Meta: A Videogame-Theoretic Perspective on\\ Learning to Learn—0
Meta-Learning: A Survey—0
Meta Learning Backpropagation And Improving It—0
Meta-learning Based Beamforming Design for MISO Downlink—0
Meta-Learning-Based Delayless Subband Adaptive Filter using Complex Self-Attention for Active Noise Control—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