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 201225 of 3569 papers

TitleStatusHype
BOML: A Modularized Bilevel Optimization Library in Python for Meta LearningCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Few-Shot Microscopy Image Cell SegmentationCode1
Few-Shot Named Entity Recognition: A Comprehensive StudyCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Few-Shot Object Detection and Viewpoint Estimation for Objects in the WildCode1
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-LearningCode1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person RetrievalCode1
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
Can Learned Optimization Make Reinforcement Learning Less Difficult?Code1
Few-Shot Scene Adaptive Crowd Counting Using Meta-LearningCode1
CD-FSOD: A Benchmark for Cross-domain Few-shot Object DetectionCode1
Few-shot Text Classification with Distributional SignaturesCode1
Adaptive Multi-Teacher Knowledge Distillation with Meta-LearningCode1
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed RecognitionCode1
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
A picture of the space of typical learnable tasksCode1
Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed AdaptationCode1
A Structured Dictionary Perspective on Implicit Neural RepresentationsCode1
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in ContextCode1
Diffusion-Based Neural Network Weights GenerationCode1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Domain Adaptive Few-Shot Open-Set LearningCode1
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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