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 371–380 of 3569 papers

TitleStatusHype
Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype EnhancementCode1
Influence-Balanced Loss for Imbalanced Visual ClassificationCode1
Instance Credibility Inference for Few-Shot LearningCode1
Interventional Few-Shot LearningCode1
Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-IdentificationCode1
Knowledge-Aware Meta-learning for Low-Resource Text ClassificationCode1
AutoDebias: Learning to Debias for RecommendationCode1
Laplacian Regularized Few-Shot LearningCode1
Learned Initializations for Optimizing Coordinate-Based Neural RepresentationsCode1
Context-Aware Meta-LearningCode1
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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