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 2161–2170 of 3569 papers

TitleStatusHype
Effective Meta-Regularization by Kernelized Proximal Regularization—0
On the Practical Consistency of Meta-Reinforcement Learning Algorithms—0
Generative vs. Discriminative: Rethinking The Meta-Continual LearningCode0
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery—0
Variational Continual Bayesian Meta-Learning—0
MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering—0
Leveraging The Topological Consistencies of Learning in Deep Neural Networks—0
Confounder Identification-free Causal Visual Feature Learning—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Joint inference and input optimization in equilibrium networksCode0
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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