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 2576–2600 of 3569 papers

TitleStatusHype
Meta-Ensemble Parameter Learning—0
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning—0
MetaFAP: Meta-Learning for Frequency Agnostic Prediction of Metasurface Properties—0
Meta Feature Modulator for Long-tailed Recognition—0
Meta-Federated Learning: A Novel Approach for Real-Time Traffic Flow Management—0
Meta Federated Reinforcement Learning for Distributed Resource Allocation—0
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning—0
MetaFollower: Adaptable Personalized Autonomous Car Following—0
Meta-Forecasting by combining Global Deep Representations with Local Adaptation—0
Meta-forests: Domain generalization on random forests with meta-learning—0
Meta-free few-shot learning via representation learning with weight averaging—0
MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning—0
MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation—0
MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning—0
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments—0
Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks—0
Meta Generative Attack on Person Reidentification—0
Meta Generative Flow Networks with Personalization for Task-Specific Adaptation—0
MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration—0
Meta Gradient Boosting Neural Networks—0
MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion—0
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation—0
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation—0
Meta-Inductive Node Classification across Graphs—0
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting—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