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 176200 of 3569 papers

TitleStatusHype
A Large Scale Search Dataset for Unbiased Learning to RankCode1
Automating Continual LearningCode1
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel LearningCode1
DynaVSR: Dynamic Adaptive Blind Video Super-ResolutionCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
BaMBNet: A Blur-aware Multi-branch Network for Defocus DeblurringCode1
Empirical Bayes Transductive Meta-Learning with Synthetic GradientsCode1
Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype EnhancementCode1
Data Augmentation for Meta-LearningCode1
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural NetworksCode1
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-LearningCode1
Bayesian Model-Agnostic Meta-LearningCode1
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-LearningCode1
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary GeometriesCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Fast and Efficient Local Search for Genetic Programming Based Loss Function LearningCode1
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-startCode1
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot SegmentationCode1
Federated Reconstruction: Partially Local Federated LearningCode1
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-LearningCode1
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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