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 2261–2270 of 3569 papers

TitleStatusHype
Adaptive Submodular Meta-Learning—0
Adaptive Task Sampling for Meta-Learning—0
Adaptive Uncertainty Quantification for Scenario-based Control Using Meta-learning of Bayesian Neural Networks—0
Adaptive Variance Based Label Distribution Learning For Facial Age Estimation—0
A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies—0
A Data-Efficient Mutual Information Neural Estimator for Statistical Dependency Testing—0
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning—0
Addressing Ambiguity of Emotion Labels Through Meta-Learning—0
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment—0
A DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers—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