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

Showing 35013510 of 3569 papers

TitleStatusHype
TGG: Transferable Graph Generation for Zero-shot and Few-shot LearningCode0
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDCode0
Predicting Configuration Performance in Multiple Environments with Sequential Meta-learningCode0
Few-Shot Learning with Global Class RepresentationsCode0
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium PropagationCode0
Cost Adaptation for Robust Decentralized Swarm BehaviourCode0
Adaptive Gradient-Based Meta-Learning MethodsCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Cooperative Meta-Learning with Gradient AugmentationCode0
Adaptive Fine-Grained Sketch-Based Image RetrievalCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified