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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3051–3060 of 3569 papers

TitleStatusHype
Model Selection for Cross-Lingual TransferCode0
Learning to Self-Train for Semi-Supervised Few-Shot ClassificationCode0
Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task DivisionCode0
Episodic Multi-Task Learning with Heterogeneous Neural ProcessesCode0
Multi-task Meta Label Correction for Time Series PredictionCode0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based TrainingCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
Centroids Matching: an efficient Continual Learning approach operating in the embedding spaceCode0
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patientsCode0
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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