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 2091–2100 of 3569 papers

TitleStatusHype
Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Receviers—0
Transfering Hierarchical Structure with Dual Meta Imitation Learning—0
Transfer Learning for Algorithm Recommendation—0
Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning—0
Transfer Learning for Finetuning Large Language Models—0
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users—0
Transfer Meta-Learning: Information-Theoretic Bounds and Information Meta-Risk Minimization—0
Transferring Hierarchical Structure with Dual Meta Imitation Learning—0
Transferring SLU Models in Novel Domains—0
Transformation Invariant Few-Shot Object Detection—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