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 2871–2880 of 3569 papers

TitleStatusHype
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search—0
A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems—0
Adaptive Task Sampling for Meta-Learning—0
Contextualizing Enhances Gradient Based Meta LearningCode0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
Collision Avoidance Robotics Via Meta-Learning (CARML)Code0
Learning to Learn with Variational Information Bottleneck for Domain Generalization—0
Top-Related Meta-Learning Method for Few-Shot Object DetectionCode0
Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification—0
Submodular Meta-LearningCode0
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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