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 1076–1100 of 3569 papers

TitleStatusHype
Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images—0
Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models—0
MetaUE: Model-based Meta-learning for Underwater Image EnhancementCode0
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning—0
Anomaly Detection with Ensemble of Encoder and Decoder—0
Multi-task Meta Label Correction for Time Series PredictionCode0
Meta-learning Control Variates: Variance Reduction with Limited DataCode0
Structured State Space Models for In-Context Reinforcement LearningCode1
On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level—0
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance—0
Scenario-Agnostic Zero-Trust Defense with Explainable Threshold Policy: A Meta-Learning Approach—0
Knowledge-embedded meta-learning model for lift coefficient prediction of airfoils—0
MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning—0
Time Associated Meta Learning for Clinical Prediction—0
Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection—0
Self-tuning hyper-parameters for unsupervised cross-lingual tokenization—0
A Meta-Learning Approach to Predicting Performance and Data Requirements—0
Optimization-Based Deep learning methods for Magnetic Resonance Imaging Reconstruction and SynthesisCode0
Learning to Adapt to Online Streams with Distribution Shifts—0
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive LearningCode1
STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled TablesCode1
Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis—0
First-order ANIL provably learns representations despite overparametrization—0
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start RecommendationCode0
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot LearningCode1
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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