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

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Showing 1751–1760 of 3569 papers

TitleStatusHype
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems—0
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics—0
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks—0
LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification—0
Learning Not to Learn: Nature versus Nurture in Silico—0
Distribution Embedding Network for Meta-Learning with Variable-Length Input—0
Learning Neural Processes on the Fly—0
Distributionally robust minimization in meta-learning for system identification—0
Learning Modality Knowledge Alignment for Cross-Modality Transfer—0
Task-Robust Model-Agnostic Meta-Learning—0
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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