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 2631–2640 of 3569 papers

TitleStatusHype
Unsupervised Task Clustering for Multi-Task Reinforcement LearningCode0
A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning—0
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains—0
Uncertain Out-of-Domain Generalization—0
Attacking Few-Shot Classifiers with Adversarial Support Sets—0
Towards Robust Graph Neural Networks against Label Noise—0
Meta-Learning with Implicit Processes—0
Towards Learning to Remember in Meta Learning of Sequential Domains—0
Auto-view contrastive learning for few-shot image recognition—0
Bayesian Meta-Learning for Few-Shot 3D Shape Completion—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