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 18511860 of 3569 papers

TitleStatusHype
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems0
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round0
SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning0
Augmentation Learning for Semi-Supervised Classification0
Centroids Matching: an efficient Continual Learning approach operating in the embedding spaceCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-LearningCode0
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge0
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity AnalysisCode0
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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