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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2331–2340 of 3569 papers

TitleStatusHype
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning—0
Rapid Model Architecture Adaption for Meta-Learning—0
Bootstrapped Meta-LearningCode0
Integrated and Adaptive Guidance and Control for Endoatmospheric Missiles via Reinforcement Learning—0
Learning Fast Sample Re-weighting Without Reward Data—0
Few-shot Learning via Dependency Maximization and Instance Discriminant Analysis—0
Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach—0
Weakly Supervised Few-Shot Segmentation Via Meta-Learning—0
Prototype-Guided Memory Replay for Continual Learning—0
Continual learning under domain transfer with sparse synaptic bursting—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