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 791–800 of 3569 papers

TitleStatusHype
Adaptive Physics-informed Neural Networks: A Survey—0
Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain GeneralizationCode0
GenMetaLoc: Learning to Learn Environment-Aware Fingerprint Generation for Sample Efficient Wireless Localization—0
FS-SS: Few-Shot Learning for Fast and Accurate Spike Sorting of High-channel Count Probes—0
HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks—0
Meta-Learning Neural Mechanisms rather than Bayesian Priors—0
LIFT: Latent Implicit Functions for Task- and Data-Agnostic Encoding—0
MetaFAP: Meta-Learning for Frequency Agnostic Prediction of Metasurface Properties—0
Riemannian Geometric-based Meta Learning—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—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