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 631640 of 3569 papers

TitleStatusHype
A General Descent Aggregation Framework for Gradient-based Bi-level OptimizationCode1
Fast Adaptation to Super-Resolution Networks via Meta-LearningCode1
ArtFID: Quantitative Evaluation of Neural Style TransferCode1
MetaHDR: Model-Agnostic Meta-Learning for HDR Image ReconstructionCode1
Fast and Efficient Local Search for Genetic Programming Based Loss Function LearningCode1
Meta Pseudo LabelsCode1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesCode1
Fast Context Adaptation via Meta-LearningCode1
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
Meta Soft Label Generation for Noisy LabelsCode1
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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