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 851860 of 3569 papers

TitleStatusHype
Learning to adapt: a meta-learning approach for speaker adaptationCode0
Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain GeneralizationCode0
MetaAdvDet: Towards Robust Detection of Evolving Adversarial AttacksCode0
DiCE: The Infinitely Differentiable Monte Carlo EstimatorCode0
DiCE: The Infinitely Differentiable Monte-Carlo EstimatorCode0
Adaptive Mixing of Auxiliary Losses in Supervised LearningCode0
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learningCode0
Learning to Evolve on Dynamic GraphsCode0
A Unified Meta-Learning Framework for Dynamic Transfer LearningCode0
Detecting Sockpuppetry on Wikipedia Using 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