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 171–180 of 3569 papers

TitleStatusHype
AugFL: Augmenting Federated Learning with Pretrained ModelsCode0
Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection—0
Category-level Meta-learned NeRF Priors for Efficient Object Mapping—0
PABBO: Preferential Amortized Black-Box OptimizationCode0
Model-Agnostic Meta-Policy Optimization via Zeroth-Order Estimation: A Linear Quadratic Regulator Perspective—0
Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks—0
Learning to Generalize without Bias for Open-Vocabulary Action Recognition—0
Online Meta-learning for AutoML in Real-time (OnMAR)—0
AutoML for Multi-Class Anomaly Compensation of Sensor DriftCode0
FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real UsersCode1
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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