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 2781–2790 of 3569 papers

TitleStatusHype
How Important is the Train-Validation Split in Meta-Learning?—0
Meta-Active Learning for Node Response Prediction in Graphs—0
Domain Agnostic Learning for Unbiased Authentication—0
Few-shot Learning for Spatial Regression—0
Characterizing Policy Divergence for Personalized Meta-Reinforcement Learning—0
Learning Not to Learn: Nature versus Nurture in Silico—0
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing—0
A Survey of Deep Meta-Learning—0
Adaptive Self-training for Few-shot Neural Sequence Labeling—0
Dif-MAML: Decentralized Multi-Agent Meta-Learning—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