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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1671–1680 of 3569 papers

TitleStatusHype
Robust Meta-Representation Learning via Global Label Inference and ClassificationCode0
Reusable Options through Gradient-based Meta LearningCode0
LogAnMeta: Log Anomaly Detection Using Meta Learning—0
End to End Generative Meta Curriculum Learning For Medical Data Augmentation—0
Robust and Resource-efficient Machine Learning Aided Viewport Prediction in Virtual Reality—0
Asynchronous Distributed Bilevel OptimizationCode0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving—0
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs—0
One-shot skill assessment in high-stakes domains with limited data via meta learningCode0
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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