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 891–900 of 3569 papers

TitleStatusHype
Efficient Curvature-Aware Hypergradient Approximation for Bilevel Optimization—0
DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning—0
Efficient Federated Meta-Learning over Multi-Access Wireless Networks—0
Efficient Gaussian Neural Processes for Regression—0
DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognition—0
FedNAS: Federated Deep Learning via Neural Architecture Search—0
FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks—0
DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing—0
Federated Neural Architecture Search with Model-Agnostic Meta Learning—0
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data—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