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 1626–1650 of 3569 papers

TitleStatusHype
Auxiliary learning induced graph convolutional networks—0
A Meta Learning Approach to Discerning Causal Graph Structure—0
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments—0
Auto-view contrastive learning for few-shot image recognition—0
Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration—0
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments—0
A Comprehensive Overview and Survey of Recent Advances in Meta-Learning—0
A behavioural transformer for effective collaboration between a robot and a non-stationary human—0
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition—0
Domain-Generalized Textured Surface Anomaly Detection—0
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning—0
Domain Generalization through Meta-Learning: A Survey—0
Learning to Generalize to Unseen Tasks with Bilevel Optimization—0
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation—0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters—0
A Meta-Learning Approach for Medical Image Registration—0
Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition—0
Domain Generalization: A Survey—0
Domain-Free Adversarial Splitting for Domain Generalization—0
Domain Agnostic Learning for Unbiased Authentication—0
A Meta-Learning Approach for Few-Shot (Dis)Agreement Identification in Online Discussions—0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Learning to Cope with Adversarial Attacks—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