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 3131–3140 of 3569 papers

TitleStatusHype
NormGrad: Finding the Pixels that Matter for Training—0
Meta-learning for fast classifier adaptation to new users of Signature Verification systemsCode0
Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process—0
Model-Agnostic Meta-Learning using Runge-Kutta Methods—0
Transfer Learning for Algorithm Recommendation—0
SUM: Suboptimal Unitary Multi-task Learning Framework for Spatiotemporal Data Prediction—0
Learning to Remember from a Multi-Task Teacher—0
MetaPix: Few-Shot Video Retargeting—0
Semi Few-Shot Attribute Translation—0
When Does Self-supervision Improve Few-shot Learning?Code0
Show:102550
← PrevPage 314 of 357Next →
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