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 2401–2425 of 3569 papers

TitleStatusHype
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism—0
A Simple Recipe to Meta-Learn Forward and Backward Transfer—0
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm—0
A Single-Timescale Method for Stochastic Bilevel Optimization—0
Assessing two novel distance-based loss functions for few-shot image classification—0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation—0
A statistical physics framework for optimal learning—0
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning—0
A Student-Teacher Architecture for Dialog Domain Adaptation under the Meta-Learning Setting—0
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning—0
A supervised generative optimization approach for tabular data—0
A Survey of Deep Meta-Learning—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
A Survey of Reinforcement Learning for Optimization in Automation—0
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?—0
A Survey on Few-Shot Class-Incremental Learning—0
A Survey on Knowledge Editing of Neural Networks—0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior—0
A Systematic Review of Few-Shot Learning in Medical Imaging—0
A Theoretical Analysis of the Number of Shots in Few-Shot Learning—0
A Thorough Review on Recent Deep Learning Methodologies for Image Captioning—0
Attacking Few-Shot Classifiers with Adversarial Support Sets—0
Attacking Few-Shot Classifiers with Adversarial Support Poisoning—0
Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints—0
Show:102550
← PrevPage 97 of 143Next →
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