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 2130 of 3569 papers

TitleStatusHype
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?Code2
A Practitioner's Guide to Continual Multimodal PretrainingCode2
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image SegmentationCode2
Frustratingly Simple Few-Shot Object DetectionCode2
Generalized Inner Loop Meta-LearningCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Learning a Decision Tree Algorithm with TransformersCode2
Learning What Not to Segment: A New Perspective on Few-Shot SegmentationCode2
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2Code2
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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