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 2651–2675 of 3569 papers

TitleStatusHype
Meta-Learning Deep Energy-Based Memory Models—0
Meta-Learning-Driven Adaptive Codebook Design for Near-Field Communications—0
Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection—0
Meta-Learning Driven Lightweight Phase Shift Compression for IRS-Assisted Wireless Systems—0
Meta-Learning Driven Movable-Antenna-assisted Full-Duplex RSMA for Multi-User Communication: Performance and Optimization—0
Meta-Learning Empowered Graph Neural Networks for Radio Resource Management—0
Meta-Learning Empowered Meta-Face: Personalized Speaking Style Adaptation for Audio-Driven 3D Talking Face Animation—0
Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI—0
Meta-Learning Fast Weight Language Models—0
Meta-Learning for Adaptive Control with Automated Mirror Descent—0
Meta-Learning for Airflow Simulations with Graph Neural Networks—0
Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets—0
Meta-Learning for Black-box Optimization—0
Meta Learning for Causal Direction—0
Meta Learning for Code Summarization—0
Meta Learning for Code Summarization—0
Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer—0
Meta-Learning for Contextual Bandit Exploration—0
Meta-Learning for Domain Generalization in Semantic Parsing—0
Meta-learning for downstream aware and agnostic pretraining—0
Meta Learning for End-to-End Low-Resource Speech Recognition—0
Meta-Learning for Federated Face Recognition in Imbalanced Data Regimes—0
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem—0
Meta-Learning for Few-Shot Land Cover Classification—0
Meta Learning for Few-Shot Medical Text 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