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 3301–3325 of 3569 papers

TitleStatusHype
Marginal Debiased Network for Fair Visual Recognition—0
Margin-Based Transfer Bounds for Meta Learning with Deep Feature Embedding—0
Market-Aware Models for Efficient Cross-Market Recommendation—0
Mask-guided BERT for Few Shot Text Classification—0
Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer—0
Material synthesis through simulations guided by machine learning: a position paper—0
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning—0
Uniform Priors for Data-Efficient Transfer—0
MC-INR: Efficient Encoding of Multivariate Scientific Simulation Data using Meta-Learning and Clustered Implicit Neural Representations—0
MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
Measuring few-shot extrapolation with program induction—0
Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment—0
MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning—0
Memory-Reduced Meta-Learning with Guaranteed Convergence—0
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning—0
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance—0
Single-View 3D Object Reconstruction from Shape Priors in Memory—0
Meta-Active Learning for Node Response Prediction in Graphs—0
Meta-active Learning in Probabilistically-Safe Optimization—0
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning—0
Meta-Adaptive Prompt Distillation for Few-Shot Visual Question Answering—0
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing—0
Méta-apprentissage : classification de messages en catégories émotionnelles inconnues en entraînement (Meta-learning : Classifying Messages into Unseen Emotional Categories)—0
Meta-aprendizado para otimizacao de parametros de redes neurais—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