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 176200 of 3569 papers

TitleStatusHype
STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled TablesCode1
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive LearningCode1
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot LearningCode1
Incremental Few-Shot Object Detection via Simple Fine-Tuning ApproachCode1
Nystrom Method for Accurate and Scalable Implicit DifferentiationCode1
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image ClassificationCode1
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity RecognitionCode1
Learning from Noisy Labels with Decoupled Meta Label PurifierCode1
Procedural generation of meta-reinforcement learning tasksCode1
On Penalty-based Bilevel Gradient Descent MethodCode1
Hypernetworks build Implicit Neural Representations of SoundsCode1
Memory-Based Meta-Learning on Non-Stationary DistributionsCode1
Meta-Learning Siamese Network for Few-Shot Text ClassificationCode1
Learning to Optimize for Reinforcement LearningCode1
Few-Shot Object Detection via Variational Feature AggregationCode1
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action RecognitionCode1
MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding From Object DetectionCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
Generalizable Black-Box Adversarial Attack with Meta LearningCode1
Learning to Detect Noisy Labels Using Model-Based FeaturesCode1
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of GeneralizationCode1
Are Deep Neural Networks SMARTer than Second Graders?Code1
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen EstimatorCode1
Transductive Linear Probing: A Novel Framework for Few-Shot Node ClassificationCode1
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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