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 28812890 of 3569 papers

TitleStatusHype
Adaptive Mixing of Auxiliary Losses in Supervised LearningCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
A Partially Supervised Reinforcement Learning Framework for Visual Active SearchCode0
Proxy Network for Few Shot LearningCode0
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot LearningCode0
Generalizable and Robust Spectral Method for Multi-view Representation LearningCode0
Concurrent Meta Reinforcement LearningCode0
Learning to Explore for Stochastic Gradient MCMCCode0
Learning to Evolve on Dynamic GraphsCode0
Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set RecognitionCode0
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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