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 22012225 of 3569 papers

TitleStatusHype
Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme0
Long Short-Term Temporal Meta-learning in Online Recommendation0
MetaKernel: Learning Variational Random Features with Limited LabelsCode0
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Adaptive Domain-Specific Normalization for Generalizable Person Re-IdentificationCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing0
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
Fast Adaptive Meta-Learning for Few-Shot Image GenerationCode0
How Fine-Tuning Allows for Effective Meta-Learning0
Personalized Algorithm Generation: A Case Study in Learning ODE IntegratorsCode0
Automatic Learning to Detect Concept Drift0
Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks0
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Meta-learning using privileged information for dynamicsCode0
Cross-Domain Few-Shot Classification via Adversarial Task AugmentationCode1
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response0
Adaptive Adversarial Training for Meta Reinforcement Learning0
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph CompletionCode1
Learning to Learn to be Right for the Right Reasons0
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning0
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification0
Meta-learning for skin cancer detection using Deep Learning Techniques0
A Meta-Learning Approach for Medical Image Registration0
Stateless Neural Meta-Learning using Second-Order GradientsCode0
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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