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 14211430 of 3569 papers

TitleStatusHype
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation ExploitationCode2
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity AnalysisCode0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge0
Meta-Learning based Degradation Representation for Blind Super-ResolutionCode1
Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning0
PointFix: Learning to Fix Domain Bias for Robust Online Stereo AdaptationCode0
Meta-Interpolation: Time-Arbitrary Frame Interpolation via Dual Meta-Learning0
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks0
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations0
Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning0
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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