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 271–280 of 3569 papers

TitleStatusHype
LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification—0
Meta Curvature-Aware Minimization for Domain Generalization—0
ViSymRe: Vision-guided Multimodal Symbolic Regression—0
Label-template based Few-Shot Text Classification with Contrastive Learning—0
Three-in-One: Robust Enhanced Universal Transferable Anti-Facial Retrieval in Online Social Networks—0
Neural Networks for Threshold Dynamics ReconstructionCode0
FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image SegmentationCode2
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement LearningCode0
SeFENet: Robust Deep Homography Estimation via Semantic-Driven Feature Enhancement—0
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems—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