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

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Showing 2851–2900 of 3569 papers

TitleStatusHype
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications—0
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding—0
Federated Meta-Learning with Fast Convergence and Efficient Communication—0
Federated Meta-Learning for Traffic Steering in O-RAN—0
Federated Multi-Level Optimization over Decentralized Networks—0
Federated Neural Architecture Search with Model-Agnostic Meta Learning—0
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems—0
FedNAS: Federated Deep Learning via Neural Architecture Search—0
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization—0
Feed-Forward On-Edge Fine-tuning Using Static Synthetic Gradient Modules—0
FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks—0
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification—0
Few-Round Learning for Federated Learning—0
Few-shot acoustic event detection via meta-learning—0
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions—0
Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning—0
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains—0
Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI—0
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators—0
Few-shot Classification with Hypersphere Modeling of Prototypes—0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning—0
Few-Shot Few-Shot Learning and the role of Spatial Attention—0
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification—0
Few-Shot Histopathology Image Classification: Evaluating State-of-the-Art Methods and Unveiling Performance Insights—0
Few-Shot Human Motion Prediction via Meta-Learning—0
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information—0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis—0
Few-Shot Learning: Expanding ID Cards Presentation Attack Detection to Unknown ID Countries—0
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation—0
Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring—0
Few-shot learning for medical text: A systematic review—0
Few-Shot Learning for Road Object Detection—0
Few-shot Learning for Spatial Regression—0
Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
Few-shot Learning via Dependency Maximization and Instance Discriminant Analysis—0
Few-shot Learning with Meta Metric Learners—0
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—0
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images—0
Few-Shot Learning with Per-Sample Rich Supervision—0
FEW-SHOTLEARNING WITH WEAK SUPERVISION—0
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach—0
Few-Shot Medical Image Segmentation with Large Kernel Attention—0
Few-Shot Meta-Denoising—0
Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders—0
Few-Shot Meta-Learning on Point Cloud for Semantic Segmentation—0
Few-shot Metric Learning: Online Adaptation of Embedding for Retrieval—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