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

Showing 15611570 of 3569 papers

TitleStatusHype
Feature Extractor Stacking for Cross-domain Few-shot LearningCode0
Improved Meta Learning for Low Resource Speech Recognition0
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated SettingCode1
Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set ClassificationCode1
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus0
Meta Learning for Natural Language Processing: A Survey0
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification0
Leveraging Task Transferability to Meta-learning for Clinical Section Classification with Limited Data0
Learn to Adapt for Generalized Zero-Shot Text ClassificationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified