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 1581–1590 of 3569 papers

TitleStatusHype
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Learning to Scaffold: Optimizing Model Explanations for TeachingCode1
Few-shot learning for medical text: A systematic review—0
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot SegmentationCode1
AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks—0
Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting—0
MetaSets: Meta-Learning on Point Sets for Generalizable Representations—0
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a DifferenceCode1
Learning Task-Aware Energy Disaggregation: a Federated ApproachCode0
Self-Guided Learning to Denoise for Robust RecommendationCode1
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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