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 1501–1525 of 3569 papers

TitleStatusHype
Joint autoencoders: a flexible meta-learning framework—0
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning—0
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
Keep Learning: Self-supervised Meta-learning for Learning from Inference—0
KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification—0
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks—0
GEDI: A Graph-based End-to-end Data Imputation Framework—0
Knowledge Consolidation based Class Incremental Online Learning with Limited Data—0
Differentiable Bandit Exploration—0
A real-time battle situation intelligent awareness system based on Meta-learning & RNN—0
Knowledge-embedded meta-learning model for lift coefficient prediction of airfoils—0
Differentiable Meta-learning Model for Few-shot Semantic Segmentation—0
Knowledge-graph based Proactive Dialogue Generation with Improved Meta-Learning—0
Known Operator Learning and Hybrid Machine Learning in Medical Imaging --- A Review of the Past, the Present, and the Future—0
Know What You Don't Need: Single-Shot Meta-Pruning for Attention Heads—0
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—0
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All—0
Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation—0
Learning to Learn to Compress—0
Differentially Private Meta-Learning—0
Labeled Memory Networks for Online Model Adaptation—0
Contextual Stochastic Bilevel Optimization—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification—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