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 471–480 of 3569 papers

TitleStatusHype
Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervisionCode1
GM-DF: Generalized Multi-Scenario Deepfake DetectionCode0
Pairwise Difference Learning for ClassificationCode1
Meta Learning for Efficient Fine-Tuning of Large Language ModelsCode0
Learning Modality Knowledge Alignment for Cross-Modality Transfer—0
Toward Universal Medical Image Registration via Sharpness-Aware Meta-Continual LearningCode0
Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks—0
Meta-learning and Data Augmentation for Stress Testing Forecasting ModelsCode0
Exploring Cross-Domain Few-Shot Classification via Frequency-Aware PromptingCode0
Automated Privacy-Preserving Techniques via Meta-LearningCode0
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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