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 2251–2300 of 3569 papers

TitleStatusHype
Learning to Filter: Siamese Relation Network for Robust TrackingCode1
Fast-adapting and Privacy-preserving Federated Recommender System—0
Emotional RobBERT and Insensitive BERTje: Combining Transformers and Affect Lexica for Dutch Emotion Detection—0
Cross-Lingual Transfer with MAML on Trees—0
Keep Learning: Self-supervised Meta-learning for Learning from Inference—0
Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object DetectionCode1
Conditional Meta-Learning of Linear Representations—0
MT3: Meta Test-Time Training for Self-Supervised Test-Time AdaptionCode1
Improved Meta-Learning Training for Speaker Verification—0
StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval—0
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot LearningCode1
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement LearningCode1
A Meta-Reinforcement Learning Approach to Process Control—0
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain AdaptationCode1
Meta-Learned Invariant Risk Minimization—0
One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video—0
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual RecognitionCode1
Using Meta-learning to Recommend Process Discovery MethodsCode0
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks—0
Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation ExploitationCode1
MetaHDR: Model-Agnostic Meta-Learning for HDR Image ReconstructionCode1
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings—0
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks—0
Meta-learning of Pooling Layers for Character RecognitionCode0
Augmenting Supervised Learning by Meta-learning Unsupervised Local Rules—0
Set-to-Sequence Methods in Machine Learning: a Review—0
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot LearningCode0
Accelerating Online Reinforcement Learning via Model-Based Meta-Learning—0
How to distribute data across tasks for meta-learning?—0
Evolving parametrized Loss for Image Classification Learning on Small Datasets—0
Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning—0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—0
Learning where to learn—0
Meta Learning for Multi-agent Communication—0
Meta-learning as Learning the Meta: A Videogame-Theoretic Perspective on\\ Learning to Learn—0
FEW-SHOTLEARNING WITH WEAK SUPERVISION—0
Meta-Learning for Planning: Automatic Synthesis of Sample Based Planners—0
Uncertainty-guided Model Generalization to Unseen Domains—0
A Review on Semi-Supervised Relation Extraction—0
Population-Based Evolution Optimizes a Meta-Learning Objective—0
Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing—0
SMIL: Multimodal Learning with Severely Missing ModalityCode1
Scalable Online Recurrent Learning Using Columnar Neural NetworksCode0
u-cf2vec: Representation Learning for Personalized Algorithm Selection in Recommender Systems—0
MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic SegmentationCode1
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning—0
Meta-Learning with MAML on Trees—0
Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-IdentificationCode1
Adaptive-Control-Oriented Meta-Learning for Nonlinear SystemsCode1
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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