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 1041–1050 of 3569 papers

TitleStatusHype
AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation—0
Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation—0
ALT: An Automatic System for Long Tail Scenario Modeling—0
DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining—0
AutoLoss: Learning Discrete Schedule for Alternate Optimization—0
Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning—0
Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes—0
FORML: A Riemannian Hessian-free Method for Meta-learning on Stiefel Manifolds—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—0
Generating Pseudo-labels Adaptively for Few-shot Model-Agnostic Meta-Learning—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