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 1976–2000 of 3569 papers

TitleStatusHype
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification—0
Meta-X_NLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation—0
CML: A Contrastive Meta Learning Method to Estimate Human Label Confidence Scores and Reduce Data Collection Cost—0
Leveraging Task Transferability to Meta-learning for Clinical Section Classification with Limited Data—0
Self-Programming Artificial Intelligence Using Code-Generating Language Models—0
Fast and Scalable Human Pose Estimation using mmWave Point Cloud—0
Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection—0
Adaptable Text Matching via Meta-Weight Regulator—0
Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification—0
Meta-free few-shot learning via representation learning with weight averaging—0
Reinforcement Teaching—0
Skill-based Meta-Reinforcement Learning—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Few-shot learning for medical text: A systematic review—0
AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks—0
Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting—0
MetaSets: Meta-Learning on Point Sets for Generalizable Representations—0
Learning Task-Aware Energy Disaggregation: a Federated ApproachCode0
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization—0
A Simple Approach to Adversarial Robustness in Few-shot Image ClassificationCode0
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification—0
Neuronal diversity can improve machine learning for physics and beyondCode0
Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual LearningCode0
Interval Bound Interpolation for Few-shot Learning with Few TasksCode0
Model Based Meta Learning of Critics for Policy Gradients—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