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 32113220 of 3569 papers

TitleStatusHype
Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model GeneralizationCode0
Does MAML Only Work via Feature Re-use? A Data Centric PerspectiveCode0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence RatesCode0
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal ArmsCode0
Meta-learning Control Variates: Variance Reduction with Limited DataCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Adaptive Cascading Network for Continual Test-Time AdaptationCode0
Meta Learning Deep Visual Words for Fast Video Object SegmentationCode0
System Prompt Optimization with Meta-LearningCode0
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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