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 1111–1120 of 3569 papers

TitleStatusHype
Deep neural network ensemble by data augmentation and bagging for skin lesion classification—0
Attentive Feature Reuse for Multi Task Meta learning—0
Algorithm Selection Framework for Cyber Attack Detection—0
A Closer Look at Prototype Classifier for Few-shot Image Classification—0
Generating Personalized Dialogue via Multi-Task Meta-Learning—0
Deep Metric Learning via Adaptive Learnable Assessment—0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments—0
Deep Meta-Learning: Learning to Learn in the Concept Space—0
Deep Meta-learning in Recommendation Systems: A Survey—0
Attention-based Few-Shot Person Re-identification Using 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