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 1631–1640 of 3569 papers

TitleStatusHype
Cross-Domain Few-Shot Semantic SegmentationCode1
Pre-training to Match for Unified Low-shot Relation ExtractionCode1
Multidimensional Belief Quantification for Label-Efficient Meta-LearningCode0
Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel DecompositionCode1
Domain-Generalized Textured Surface Anomaly Detection—0
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation—0
Meta-X_NLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and GenerationCode0
Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-LearningCode0
Meta-Learning for Online Update of Recommender SystemsCode0
Negative Inner-Loop Learning Rates Learn Universal Features—0
Show:102550
← PrevPage 164 of 357Next →
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