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 24512460 of 3569 papers

TitleStatusHype
Learning to Learn Semantic Factors in Heterogeneous Image Classification0
Méta-apprentissage : classification de messages en catégories émotionnelles inconnues en entraînement (Meta-learning : Classifying Messages into Unseen Emotional Categories)0
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks0
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent0
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning0
Short-Term Stock Price-Trend Prediction Using Meta-Learning0
Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification0
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images0
Learning Generative Prior with Latent Space Sparsity Constraints0
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications0
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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