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 35113520 of 3569 papers

TitleStatusHype
Similarity of Classification TasksCode0
Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-LearningCode0
Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-LearningCode0
Automated Privacy-Preserving Techniques via Meta-LearningCode0
Simple Domain Generalization Methods are Strong Baselines for Open Domain GeneralizationCode0
Few-shot Conformal Prediction with Auxiliary TasksCode0
AutoLoss: Learning Discrete Schedules for Alternate OptimizationCode0
Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative EmbeddingsCode0
MetaPix: Domain Transfer for Semantic Segmentation by Meta Pixel WeightingCode0
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
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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