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 2991–3000 of 3569 papers

TitleStatusHype
MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation—0
Learning to Learn Single Domain GeneralizationCode0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
On Infinite-Width HypernetworksCode0
On-the-Fly Adaptation of Source Code Models using Meta-Learning—0
CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context—0
Weighted Meta-Learning—0
Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill PrimitivesCode0
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification—0
CAFENet: Class-Agnostic Few-Shot Edge Detection Network—0
Show:102550
← PrevPage 300 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