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 2881–2890 of 3569 papers

TitleStatusHype
Online Parameter-Free Learning of Multiple Low Variance TasksCode0
Towards Cross-Granularity Few-Shot Learning: Coarse-to-Fine Pseudo-Labeling with Visual-Semantic Meta-Embedding—0
Meta-Learning Requires Meta-Augmentation—0
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification—0
Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing—0
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant NetworkCode0
Meta-Learning with Network Pruning—0
Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network—0
Meta-active Learning in Probabilistically-Safe Optimization—0
Meta-Learning Divergences of Variational Inference—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