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 26–50 of 3569 papers

TitleStatusHype
FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image SegmentationCode2
TensorFlow Quantum: A Software Framework for Quantum Machine LearningCode2
Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-OptimizationCode2
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series DataCode2
Transformers learn in-context by gradient descentCode2
Discovering Evolution Strategies via Meta-Black-Box OptimizationCode2
Fine-Grained Prototypes Distillation for Few-Shot Object DetectionCode2
Decomposed Meta-Learning for Few-Shot Named Entity RecognitionCode2
Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse ProblemsCode2
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with TransformersCode2
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?Code2
Efficient and Modular Implicit DifferentiationCode2
A Practitioner's Guide to Continual Multimodal PretrainingCode2
Generalized Inner Loop Meta-LearningCode2
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