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 2476–2500 of 3569 papers

TitleStatusHype
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Adaptive Submodular Meta-Learning—0
Performance-Weighed Policy Sampling for Meta-Reinforcement Learning—0
Cold-start Sequential Recommendation via Meta Learner—0
Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning—0
Portrait Neural Radiance Fields from a Single Image—0
Adversarial Meta-Learning of Gamma-Minimax Estimators That Leverage Prior KnowledgeCode0
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting—0
Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test—0
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learningCode0
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs—0
Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules—0
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks—0
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?—0
Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-AlignmentCode0
Forecast with Forecasts: Diversity Matters—0
Learned Initializations for Optimizing Coordinate-Based Neural RepresentationsCode1
Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution—0
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning—0
Margin-Based Transfer Bounds for Meta Learning with Deep Feature Embedding—0
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains—0
Meta-Information Guided Meta-Learning for Few-Shot Relation ClassificationCode0
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-IdentificationCode1
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction—0
Learning to Adapt to Evolving DomainsCode1
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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