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 41–50 of 3569 papers

TitleStatusHype
Gödel Agent: A Self-Referential Agent Framework for Recursive Self-ImprovementCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Learning What Not to Segment: A New Perspective on Few-Shot SegmentationCode2
MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box OptimizationCode2
Learning Deep Time-index Models for Time Series ForecastingCode2
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand predictionCode2
Frustratingly Simple Few-Shot Object DetectionCode2
MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-LearningCode2
Neural-Fly Enables Rapid Learning for Agile Flight in Strong WindsCode2
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object DetectionCode1
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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