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

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Showing 2801–2810 of 3569 papers

TitleStatusHype
Bayesian Online Meta-Learning—0
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms—0
Provably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning—0
Learning to Adapt Multi-View Stereo by Self-Supervision—0
Domain Generalization via Semi-supervised Meta LearningCode0
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control—0
A Primal-Dual Subgradient Approachfor Fair Meta LearningCode0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey—0
Attribute Propagation Network for Graph Zero-shot Learning—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified