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 521530 of 3569 papers

TitleStatusHype
An Enhanced Span-based Decomposition Method for Few-Shot Sequence LabelingCode1
Direct Differentiable Augmentation SearchCode1
Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object DetectionCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Discovering Reinforcement Learning AlgorithmsCode1
Discovering Temporally-Aware Reinforcement Learning AlgorithmsCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the WildCode1
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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