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 1551–1600 of 3569 papers

TitleStatusHype
BayesPCN: A Continually Learnable Predictive Coding Associative MemoryCode0
Persian Natural Language Inference: A Meta-learning approachCode0
Meta-Learning Sparse Compression Networks—0
Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling—0
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks—0
Meta Balanced Network for Fair Face Recognition—0
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities—0
Neural-Fly Enables Rapid Learning for Agile Flight in Strong WindsCode2
Multi-Environment Meta-Learning in Stochastic Linear Bandits—0
Warm-starting DARTS using meta-learning—0
Feature Extractor Stacking for Cross-domain Few-shot LearningCode0
Improved Meta Learning for Low Resource Speech Recognition—0
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated SettingCode1
Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set ClassificationCode1
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus—0
Meta Learning for Natural Language Processing: A Survey—0
On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification—0
Leveraging Task Transferability to Meta-learning for Clinical Section Classification with Limited Data—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
Learn to Adapt for Generalized Zero-Shot Text ClassificationCode1
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
Meta-free few-shot learning via representation learning with weight averaging—0
Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification—0
Reinforcement Teaching—0
Skill-based Meta-Reinforcement Learning—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Learning to Scaffold: Optimizing Model Explanations for TeachingCode1
Few-shot learning for medical text: A systematic review—0
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot SegmentationCode1
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
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a DifferenceCode1
MetaSets: Meta-Learning on Point Sets for Generalizable Representations—0
Self-Guided Learning to Denoise for Robust RecommendationCode1
Control-oriented meta-learningCode1
Learning Task-Aware Energy Disaggregation: a Federated ApproachCode0
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization—0
Decomposed Meta-Learning for Few-Shot Named Entity RecognitionCode2
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
Pin the Memory: Learning to Generalize Semantic SegmentationCode1
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection—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