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 30713080 of 3569 papers

TitleStatusHype
Meta-Graph: Few Shot Link Prediction via Meta LearningCode0
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning0
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift0
Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems0
Continuous Meta-Learning without TasksCode0
Conditional Super Learner0
Meta-Learning Initializations for Image SegmentationCode0
Associative Alignment for Few-shot Image ClassificationCode0
Meta-Learning without MemorizationCode0
Unsupervised Curricula for Visual Meta-Reinforcement Learning0
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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