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

Showing 29012925 of 3569 papers

TitleStatusHype
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networksCode0
Learning to Defer to a Population: A Meta-Learning ApproachCode0
Comprehensive Fair Meta-learned Recommender SystemCode0
Reevaluating Meta-Learning Optimization Algorithms Through Contextual Self-ModulationCode0
Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum AlgorithmsCode0
Spectral Convolutional Conditional Neural ProcessesCode0
Learning to Customize Model Structures for Few-shot Dialogue Generation TasksCode0
Component-Based Fairness in Face Attribute Classification with Bayesian Network-informed Meta LearningCode0
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning ApproachCode0
Algorithm Selection on a Meta LevelCode0
Exploring the Similarity of Representations in Model-Agnostic Meta-LearningCode0
Exploring the similarity of medical imaging classification problemsCode0
Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial TransferabilityCode0
The least-control principle for local learning at equilibriumCode0
LeMON: Learning to Learn Multi-Operator NetworksCode0
Meta Transferring for DeblurringCode0
Two Sides of Meta-Learning Evaluation: In vs. Out of DistributionCode0
MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer AdaptersCode0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
Query-efficient Meta Attack to Deep Neural NetworksCode0
Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction PredictionCode0
Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation ModelsCode0
Learning to Segment Medical Images from Few-Shot Sparse LabelsCode0
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote SensingCode0
Learning to Continually Learn Rapidly from Few and Noisy DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified