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 1551–1575 of 3569 papers

TitleStatusHype
Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning—0
TinyReptile: TinyML with Federated Meta-Learning—0
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense—0
Meta Compositional Referring Expression Segmentation—0
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning—0
Experience-Based Evolutionary Algorithms for Expensive OptimizationCode0
ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis—0
Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning—0
SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning—0
Learning to Learn with Indispensable Connections—0
Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks—0
Online Algorithms for Hierarchical Inference in Deep Learning applications at the Edge—0
A comparison of small sample methods for Handshape RecognitionCode0
Simple Domain Generalization Methods are Strong Baselines for Open Domain GeneralizationCode0
Scalable Bayesian Meta-Learning through Generalized Implicit GradientsCode0
Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization—0
Towards Unbiased Calibration using Meta-Regularization—0
Autoregressive Conditional Neural ProcessesCode0
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization—0
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence—0
A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance ImagesCode0
Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot GeneralizationCode0
Assessor-Guided Learning for Continual EnvironmentsCode0
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting—0
A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition—0
Show:102550
← PrevPage 63 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