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 871–880 of 3569 papers

TitleStatusHype
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data—0
Extension of Transformational Machine Learning: Classification Problems—0
A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction—0
Meta-learning in healthcare: A survey—0
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation—0
Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification—0
Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification—0
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning—0
You Can Backdoor Personalized Federated LearningCode1
Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications—0
Show:102550
← PrevPage 88 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