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 7180 of 3569 papers

TitleStatusHype
Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervisionCode1
Pairwise Difference Learning for ClassificationCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Meta-Learning Loss Functions for Deep Neural NetworksCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
What Do Language Models Learn in Context? The Structured Task HypothesisCode1
GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View SynthesisCode1
Learning to Continually Learn with the Bayesian PrincipleCode1
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement LearningCode1
Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed AdaptationCode1
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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