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 2451–2500 of 3569 papers

TitleStatusHype
Learning to Learn Semantic Factors in Heterogeneous Image Classification—0
Méta-apprentissage : classification de messages en catégories émotionnelles inconnues en entraînement (Meta-learning : Classifying Messages into Unseen Emotional Categories)—0
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks—0
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent—0
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning—0
Short-Term Stock Price-Trend Prediction Using Meta-Learning—0
Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification—0
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images—0
Learning Generative Prior with Latent Space Sparsity Constraints—0
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications—0
Auxiliary learning induced graph convolutional networks—0
Meta Reinforcement Learning for Fast Adaptation of Hierarchical Policies—0
Fast Training Method for Stochastic Compositional Optimization Problems—0
Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks—0
Meta Learning the Step Size in Policy Gradient Methods—0
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection—0
Discovering Weight Initializers with Meta LearningCode0
Fast and Slow Learning of Recurrent Independent Mechanisms—0
Exploiting Adapters for Cross-lingual Low-resource Speech RecognitionCode0
Sample Efficient Linear Meta-Learning by Alternating Minimization—0
DRILL: Dynamic Representations for Imbalanced Lifelong LearningCode0
HetMAML: Task-Heterogeneous Model-Agnostic Meta-Learning for Few-Shot Learning Across Modalities—0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments—0
Meta Auxiliary Learning for Facial Action Unit Detection—0
Meta-Inductive Node Classification across Graphs—0
Exploring the Similarity of Representations in Model-Agnostic Meta-LearningCode0
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users—0
Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme—0
MetaKernel: Learning Variational Random Features with Limited LabelsCode0
Long Short-Term Temporal Meta-learning in Online Recommendation—0
Few-Shot Learning for Image Classification of Common FloraCode0
Adaptive Domain-Specific Normalization for Generalizable Person Re-IdentificationCode0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
Fast Adaptive Meta-Learning for Few-Shot Image GenerationCode0
How Fine-Tuning Allows for Effective Meta-Learning—0
Automatic Learning to Detect Concept Drift—0
Personalized Algorithm Generation: A Case Study in Learning ODE IntegratorsCode0
Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks—0
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response—0
Meta-learning using privileged information for dynamicsCode0
Adaptive Adversarial Training for Meta Reinforcement Learning—0
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
Stateless Neural Meta-Learning using Second-Order GradientsCode0
Meta-learning for skin cancer detection using Deep Learning Techniques—0
A Meta-Learning Approach for Medical Image Registration—0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
On the Influence of Masking Policies in Intermediate Pre-training—0
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot 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