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 3221–3230 of 3569 papers

TitleStatusHype
Few-Shot Meta-Denoising—0
Meta Learning for Task-Driven Video Summarization—0
ROAM: Recurrently Optimizing Tracking Model—0
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference—0
Towards meta-learning for multi-target regression problems—0
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta LearningCode0
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition—0
TARN: Temporal Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition—0
Towards Understanding Generalization in Gradient-Based Meta-Learning—0
Adaptive Prior Selection for Repertoire-based Online Adaptation in RoboticsCode0
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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