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 411–420 of 3569 papers

TitleStatusHype
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel LearningCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target DataCode1
MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding From Object DetectionCode1
Few-Shot Semantic Parsing for New PredicatesCode1
Few-shot Visual Relationship Co-localizationCode1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
MetaHTR: Towards Writer-Adaptive Handwritten Text RecognitionCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
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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