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 1621–1630 of 3569 papers

TitleStatusHype
Efficient Gradient Approximation Method for Constrained Bilevel Optimization—0
Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation TypesCode0
MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning—0
Algorithm Design for Online Meta-Learning with Task Boundary Detection—0
Meta Learning in Decentralized Neural Networks: Towards More General AI—0
Learning Generalized Zero-Shot Learners for Open-Domain Image GeolocalizationCode0
Local transfer learning from one data space to another—0
A Knowledge-Driven Meta-Learning Method for CSI Feedback—0
Robust Meta Learning for Image based tasks—0
Online Loss Function Learning—0
Show:102550
← PrevPage 163 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