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 10411050 of 3569 papers

TitleStatusHype
Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set RecognitionCode0
Dynamics of Meta-learning Representation in the Teacher-student Scenario0
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees0
Making Large Vision Language Models to be Good Few-shot Learners0
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey0
Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review0
Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer DetectionCode0
Meta-Learning Empowered Meta-Face: Personalized Speaking Style Adaptation for Audio-Driven 3D Talking Face Animation0
Learning to Explore for Stochastic Gradient MCMCCode0
Meta-Learning for Federated Face Recognition in Imbalanced Data Regimes0
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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