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 151160 of 3569 papers

TitleStatusHype
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
A picture of the space of typical learnable tasksCode1
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary GeometriesCode1
An Analysis of the Adaptation Speed of Causal ModelsCode1
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IVCode1
ARCADe: A Rapid Continual Anomaly DetectorCode1
A General Descent Aggregation Framework for Gradient-based Bi-level OptimizationCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
Control-oriented meta-learningCode1
Show:102550
← PrevPage 16 of 357Next →

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