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 1131–1140 of 3569 papers

TitleStatusHype
Algorithm Design for Online Meta-Learning with Task Boundary Detection—0
MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning—0
Local transfer learning from one data space to another—0
Learning Generalized Zero-Shot Learners for Open-Domain Image GeolocalizationCode0
A Knowledge-Driven Meta-Learning Method for CSI Feedback—0
Few-Shot Object Detection via Variational Feature AggregationCode1
Robust Meta Learning for Image based tasks—0
Online Loss Function Learning—0
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
MetaNO: How to Transfer Your Knowledge on Learning Hidden Physics—0
Show:102550
← PrevPage 114 of 357Next →
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