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

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Showing 471–480 of 3569 papers

TitleStatusHype
Data Augmentation for Meta-LearningCode1
Semi-supervised Relation Extraction via Incremental Meta Self-TrainingCode1
CURI: A Benchmark for Productive Concept Learning Under UncertaintyCode1
On Negative Interference in Multilingual Models: Findings and A Meta-Learning TreatmentCode1
Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor LearningCode1
MetaPhys: Few-Shot Adaptation for Non-Contact Physiological MeasurementCode1
Exploration in Approximate Hyper-State Space for Meta Reinforcement LearningCode1
BOML: A Modularized Bilevel Optimization Library in Python for Meta LearningCode1
Interventional Few-Shot LearningCode1
Automating Outlier Detection via Meta-LearningCode1
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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