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 2441–2450 of 3569 papers

TitleStatusHype
Learning to Segment Skin Lesions from Noisy Annotations—0
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction—0
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation—0
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning—0
Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing—0
Learning to Tune XGBoost with XGBoost—0
Learning to Unlearn for Robust Machine Unlearning—0
Learning to Update for Object Tracking with Recurrent Meta-learner—0
Learning Unsupervised Learning Rules—0
Learning via Surrogate PAC-Bayes—0
Show:102550
← PrevPage 245 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