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

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Showing 151–200 of 3569 papers

TitleStatusHype
Learning to Learn from APIs: Black-Box Data-Free Meta-LearningCode1
Zero- and Few-Shot Event Detection via Prompt-Based Meta LearningCode1
Meta-prediction Model for Distillation-Aware NAS on Unseen DatasetsCode1
Meta-Learning Online Adaptation of Language ModelsCode1
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic SegmentationCode1
Improving Convergence and Generalization Using Parameter SymmetriesCode1
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta LearningCode1
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer TasksCode1
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-LearningCode1
Meta Omnium: A Benchmark for General-Purpose Learning-to-LearnCode1
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series ForecastingCode1
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph CompletionCode1
Meta-optimized Contrastive Learning for Sequential RecommendationCode1
Meta-Learned Models of CognitionCode1
Exploring Effective Factors for Improving Visual In-Context LearningCode1
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue DistributionCode1
Meta-Learning with a Geometry-Adaptive PreconditionerCode1
DoE2Vec: Deep-learning Based Features for Exploratory Landscape AnalysisCode1
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEsCode1
Image Quality-aware Diagnosis via Meta-knowledge Co-embeddingCode1
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-LearningCode1
Learning Distortion Invariant Representation for Image Restoration from A Causality PerspectiveCode1
Structured State Space Models for In-Context Reinforcement LearningCode1
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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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