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 251275 of 3569 papers

TitleStatusHype
Learning a Formula of Interpretability to Learn Interpretable FormulasCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
An Analysis of the Adaptation Speed of Causal ModelsCode1
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object SegmentationCode1
Fast Adaptation to Super-Resolution Networks via Meta-LearningCode1
Adversarial Feature Augmentation for Cross-domain Few-shot ClassificationCode1
Dynamic Relevance Learning for Few-Shot Object DetectionCode1
DynaVSR: Dynamic Adaptive Blind Video Super-ResolutionCode1
Attentional-Biased Stochastic Gradient DescentCode1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Editing Factual Knowledge in Language ModelsCode1
Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-LearningCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
DPGN: Distribution Propagation Graph Network for Few-shot LearningCode1
DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend ForecastingCode1
Domain-General Crowd Counting in Unseen ScenariosCode1
A Structured Dictionary Perspective on Implicit Neural RepresentationsCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
AReLU: Attention-based Rectified Linear UnitCode1
Discovering Temporally-Aware Reinforcement Learning AlgorithmsCode1
Improving Generalization in Meta-learning via Task AugmentationCode1
A Simple Approach to Case-Based Reasoning in Knowledge BasesCode1
Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social MediaCode1
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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