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 181–190 of 3569 papers

TitleStatusHype
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image ClassificationCode1
Learning from Noisy Labels with Decoupled Meta Label PurifierCode1
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity RecognitionCode1
Procedural generation of meta-reinforcement learning tasksCode1
On Penalty-based Bilevel Gradient Descent MethodCode1
Hypernetworks build Implicit Neural Representations of SoundsCode1
Memory-Based Meta-Learning on Non-Stationary DistributionsCode1
Meta-Learning Siamese Network for Few-Shot Text ClassificationCode1
Learning to Optimize for Reinforcement LearningCode1
Few-Shot Object Detection via Variational Feature AggregationCode1
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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