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 1776–1800 of 3569 papers

TitleStatusHype
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical recordsCode0
CoMPS: Continual Meta Policy Search—0
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance—0
Noether Networks: Meta-Learning Useful Conserved Quantities—0
Curriculum Meta-Learning for Few-shot ClassificationCode0
MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples—0
Self-supervised Graph Learning for Occasional Group Recommendation—0
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation—0
Fast Data-Driven Adaptation of Radar Detection via Meta-Learning—0
AirDet: Few-Shot Detection without Fine-tuning for Autonomous ExplorationCode1
A Structured Dictionary Perspective on Implicit Neural RepresentationsCode1
On the Practical Consistency of Meta-Reinforcement Learning Algorithms—0
Meta Arcade: A Configurable Environment Suite for Meta-Learning—0
Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social MediaCode1
Effective Meta-Regularization by Kernelized Proximal Regularization—0
Hardware-adaptive Efficient Latency Prediction for NAS via Meta-LearningCode1
Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation—0
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery—0
Variational Continual Bayesian Meta-Learning—0
Automatic Unsupervised Outlier Model Selection—0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
Fast Training Method for Stochastic Compositional Optimization Problems—0
Meta-Learning via Learning with Distributed Memory—0
Generative vs. Discriminative: Rethinking The Meta-Continual LearningCode0
Leveraging The Topological Consistencies of Learning in Deep Neural Networks—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