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 2941–2950 of 3569 papers

TitleStatusHype
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography—0
FS-SS: Few-Shot Learning for Fast and Accurate Spike Sorting of High-channel Count Probes—0
Fully Online Meta-Learning Without Task Boundaries—0
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery—0
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals—0
Function Contrastive Learning of Transferable Meta-Representations—0
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces—0
Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification—0
Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation—0
GEDI: A Graph-based End-to-end Data Imputation Framework—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