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

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Showing 2951–2960 of 3569 papers

TitleStatusHype
Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks—0
When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications—0
A Concise Review of Recent Few-shot Meta-learning Methods—0
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks—0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
Multitask Learning with Single Gradient Step Update for Task Balancing—0
A Novel Meta Learning Framework for Feature Selection using Data Synthesis and Fuzzy Similarity—0
Meta-learning with Stochastic Linear Bandits—0
Self-Supervised Deep Visual Odometry with Online Adaptation—0
Dynamic Memory Induction Networks for Few-Shot Text Classification—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified