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 2201–2250 of 3569 papers

TitleStatusHype
Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme—0
Long Short-Term Temporal Meta-learning in Online Recommendation—0
MetaKernel: Learning Variational Random Features with Limited LabelsCode0
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Adaptive Domain-Specific Normalization for Generalizable Person Re-IdentificationCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
Fast Adaptive Meta-Learning for Few-Shot Image GenerationCode0
How Fine-Tuning Allows for Effective Meta-Learning—0
Personalized Algorithm Generation: A Case Study in Learning ODE IntegratorsCode0
Automatic Learning to Detect Concept Drift—0
Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks—0
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Meta-learning using privileged information for dynamicsCode0
Cross-Domain Few-Shot Classification via Adversarial Task AugmentationCode1
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response—0
Adaptive Adversarial Training for Meta Reinforcement Learning—0
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph CompletionCode1
Learning to Learn to be Right for the Right Reasons—0
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning—0
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification—0
Meta-learning for skin cancer detection using Deep Learning Techniques—0
A Meta-Learning Approach for Medical Image Registration—0
Stateless Neural Meta-Learning using Second-Order GradientsCode0
X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question AnsweringCode1
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?Code2
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot LearningCode0
On the Influence of Masking Policies in Intermediate Pre-training—0
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual LearningCode1
Editing Factual Knowledge in Language ModelsCode1
Generalizable No-Reference Image Quality Assessment via Deep Meta-learningCode1
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature AlignmentCode1
Learning Normal Dynamics in Videos with Meta Prototype NetworkCode1
Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling—0
Contextual HyperNetworks for Novel Feature Adaptation—0
How Sensitive are Meta-Learners to Dataset Imbalance?Code1
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency ParsingCode0
Meta-Learning Bidirectional Update RulesCode0
Direct Differentiable Augmentation SearchCode1
Towards Enabling Meta-Learning from Target ModelsCode0
Efficient time stepping for numerical integration using reinforcement learningCode0
Open Domain Generalization with Domain-Augmented Meta-Learning—0
Few-Shot Meta-Learning on Point Cloud for Semantic Segmentation—0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark—0
A Student-Teacher Architecture for Dialog Domain Adaptation under the Meta-Learning Setting—0
MetaHTR: Towards Writer-Adaptive Handwritten Text RecognitionCode1
A contrastive rule for meta-learningCode1
Thalamocortical contribution to solving credit assignment in neural systems—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