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 581590 of 3569 papers

TitleStatusHype
Concrete Subspace Learning based Interference Elimination for Multi-task Model FusionCode1
A picture of the space of typical learnable tasksCode1
Can Learned Optimization Make Reinforcement Learning Less Difficult?Code1
Improving Generalization in Meta-learning via Task AugmentationCode1
MetaDelta: A Meta-Learning System for Few-shot Image ClassificationCode1
BOIL: Towards Representation Change for Few-shot LearningCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Domain-General Crowd Counting in Unseen ScenariosCode1
Meta Discovery: Learning to Discover Novel Classes given Very Limited DataCode1
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IVCode1
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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