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 571580 of 3569 papers

TitleStatusHype
Instance Credibility Inference for Few-Shot LearningCode1
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?Code1
iTAML: An Incremental Task-Agnostic Meta-learning ApproachCode1
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation PerspectiveCode1
Meta Pseudo LabelsCode1
Learning Meta Face Recognition in Unseen DomainsCode1
Incremental Object Detection via Meta-LearningCode1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG ClassificationCode1
Learning Compositional Rules via Neural Program SynthesisCode1
Show:102550
← PrevPage 58 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