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 3521–3530 of 3569 papers

TitleStatusHype
One-Shot Visual Imitation Learning via Meta-LearningCode0
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery—0
ALCN: Meta-Learning for Contrast Normalization Applied to Robust 3D Pose Estimation—0
Meta-Learning MCMC Proposals—0
A Simple Neural Attentive Meta-LearnerCode0
Labeled Memory Networks for Online Model Adaptation—0
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning—0
A Meta-Learning Approach to One-Step Active Learning—0
Exploring the similarity of medical imaging classification problemsCode0
Meta learning Framework for Automated Driving—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