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 1076–1100 of 3569 papers

TitleStatusHype
Base Models for Parabolic Partial Differential EquationsCode0
Adaptive Cascading Network for Continual Test-Time AdaptationCode0
An Evaluation of Continual Learning for Advanced Node Semiconductor Defect Inspection—0
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments—0
Siamese Transformer Networks for Few-shot Image Classification—0
Efficient In-Context Medical Segmentation with Meta-driven Visual Prompt Selection—0
Learning to Unlearn for Robust Machine Unlearning—0
Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions—0
A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification—0
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant FactorsCode0
MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelinesCode0
DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognition—0
EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation—0
Meta-Learning and representation learner: A short theoretical note—0
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce ScenariosCode0
Pushing the Boundary: Specialising Deep Configuration Performance Learning—0
Meta-Learning Based Optimization for Large Scale Wireless Systems—0
GM-DF: Generalized Multi-Scenario Deepfake DetectionCode0
Meta Learning for Efficient Fine-Tuning of Large Language ModelsCode0
Learning Modality Knowledge Alignment for Cross-Modality Transfer—0
Toward Universal Medical Image Registration via Sharpness-Aware Meta-Continual LearningCode0
Exploring Cross-Domain Few-Shot Classification via Frequency-Aware PromptingCode0
Meta-learning and Data Augmentation for Stress Testing Forecasting ModelsCode0
Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks—0
Automated Privacy-Preserving Techniques via Meta-LearningCode0
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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