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 2701–2710 of 3569 papers

TitleStatusHype
Domain Agnostic Learning for Unbiased Authentication—0
Domain-Free Adversarial Splitting for Domain Generalization—0
Domain Generalization: A Survey—0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters—0
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation—0
Domain Generalization through Meta-Learning: A Survey—0
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning—0
Domain-Generalized Textured Surface Anomaly Detection—0
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition—0
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—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