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 22512275 of 3569 papers

TitleStatusHype
Learning to Filter: Siamese Relation Network for Robust TrackingCode1
Fast-adapting and Privacy-preserving Federated Recommender System0
Emotional RobBERT and Insensitive BERTje: Combining Transformers and Affect Lexica for Dutch Emotion Detection0
Cross-Lingual Transfer with MAML on Trees0
Keep Learning: Self-supervised Meta-learning for Learning from Inference0
Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object DetectionCode1
Conditional Meta-Learning of Linear Representations0
MT3: Meta Test-Time Training for Self-Supervised Test-Time AdaptionCode1
Improved Meta-Learning Training for Speaker Verification0
StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval0
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 Control0
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain AdaptationCode1
Meta-Learned Invariant Risk Minimization0
One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video0
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 Tasks0
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 Settings0
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks0
Meta-learning of Pooling Layers for Character RecognitionCode0
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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