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 4150 of 3569 papers

TitleStatusHype
Efficient and Modular Implicit DifferentiationCode2
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?Code2
Meta-Learning Symmetries by ReparameterizationCode2
Global Convergence and Generalization Bound of Gradient-Based Meta-Learning with Deep Neural NetsCode2
Frustratingly Simple Few-Shot Object DetectionCode2
TensorFlow Quantum: A Software Framework for Quantum Machine LearningCode2
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement LearningCode2
Generalized Inner Loop Meta-LearningCode2
Prototypical Networks for Few-shot LearningCode2
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
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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