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 551575 of 3569 papers

TitleStatusHype
Meta Dropout: Learning to Perturb Latent Features for GeneralizationCode1
Towards Fast Adaptation of Neural Architectures with Meta LearningCode1
Physarum Powered Differentiable Linear Programming Layers and ApplicationsCode1
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense DisambiguationCode1
Empirical Bayes Transductive Meta-Learning with Synthetic GradientsCode1
Learning a Formula of Interpretability to Learn Interpretable FormulasCode1
Model-Based Meta-Reinforcement Learning for Flight with Suspended PayloadsCode1
Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude LoopCode1
Regularizing Meta-Learning via Gradient DropoutCode1
Meta-Learning in Neural Networks: A SurveyCode1
MetaIQA: Deep Meta-learning for No-Reference Image Quality AssessmentCode1
MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-LearningCode1
QuantNet: Transferring Learning Across Systematic Trading StrategiesCode1
Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length PairsCode1
There and Back Again: Revisiting Backpropagation Saliency MethodsCode1
Scene-Adaptive Video Frame Interpolation via Meta-LearningCode1
MetaPoison: Practical General-purpose Clean-label Data PoisoningCode1
DPGN: Distribution Propagation Graph Network for Few-shot LearningCode1
Multi-Task Reinforcement Learning with Soft ModularizationCode1
Efficient Domain Generalization via Common-Specific Low-Rank DecompositionCode1
Instance Credibility Inference for Few-Shot LearningCode1
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?Code1
iTAML: An Incremental Task-Agnostic Meta-learning ApproachCode1
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation PerspectiveCode1
Meta Pseudo LabelsCode1
Show:102550
← PrevPage 23 of 143Next →

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