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 1651–1675 of 3569 papers

TitleStatusHype
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification—0
Learning to Generalize to Unseen Tasks with Bilevel Optimization—0
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation—0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters—0
A Meta-Learning Approach for Medical Image Registration—0
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects—0
Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition—0
Learning to Learn a Cold-start Sequential Recommender—0
Domain Generalization: A Survey—0
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification—0
Domain-Free Adversarial Splitting for Domain Generalization—0
Domain Agnostic Learning for Unbiased Authentication—0
A Meta-Learning Approach for Few-Shot (Dis)Agreement Identification in Online Discussions—0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Learning to Cope with Adversarial Attacks—0
Domain Adaptation in Dialogue Systems using Transfer and Meta-Learning—0
AutoML for Contextual Bandits—0
A Meta-Learning Approach for Custom Model Training—0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification—0
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer—0
Is Nash Equilibrium Approximator Learnable?—0
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?—0
Learning to Classify Intents and Slot Labels Given a Handful of Examples—0
Learning to Bound the Multi-class Bayes Error—0
AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks—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