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 2481–2490 of 3569 papers

TitleStatusHype
Local transfer learning from one data space to another—0
LogAnMeta: Log Anomaly Detection Using Meta Learning—0
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models—0
Long Short-Term Temporal Meta-learning in Online Recommendation—0
Looking back to lower-level information in few-shot learning—0
Loss Function Learning for Domain Generalization by Implicit Gradient—0
Loss meta-learning for forecasting—0
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing—0
Low-Resource Multilingual and Zero-Shot Multispeaker TTS—0
Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?—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