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 2971–2980 of 3569 papers

TitleStatusHype
RECOD Titans at ISIC Challenge 2017Code0
Minimax and Neyman-Pearson Meta-Learning for Outlier LanguagesCode0
MALIBO: Meta-learning for Likelihood-free Bayesian OptimizationCode0
Adaptive Conditional Quantile Neural ProcessesCode0
MAML-CL: Edited Model-Agnostic Meta-Learning for Continual LearningCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
TIDE: Test Time Few Shot Object DetectionCode0
Learning Low-Dimensional Embeddings for Black-Box OptimizationCode0
Evaluating recommender systems for AI-driven biomedical informaticsCode0
Manifold meta-learning for reduced-complexity neural system identificationCode0
Show:102550
← PrevPage 298 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