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

Showing 911920 of 3569 papers

TitleStatusHype
Attentional Meta-learners for Few-shot Polythetic ClassificationCode0
Learning advisor networks for noisy image classificationCode0
Image Deformation Meta-Networks for One-Shot LearningCode0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit FeedbackCode0
Analyzing the Effectiveness of Quantum Annealing with Meta-LearningCode0
Zero-shot task adaptation by homoiconic meta-mappingCode0
Learning Generalized Zero-Shot Learners for Open-Domain Image GeolocalizationCode0
Learning Task-Aware Energy Disaggregation: a Federated ApproachCode0
Deep Learning Theory Review: An Optimal Control and Dynamical Systems PerspectiveCode0
Show:102550
← PrevPage 92 of 357Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified