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 476500 of 3569 papers

TitleStatusHype
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in ContextCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive LearningCode1
Generalizable Implicit Neural Representations via Instance Pattern ComposersCode1
Generalizable No-Reference Image Quality Assessment via Deep Meta-learningCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learningCode1
Gradient-based Hyperparameter Optimization Over Long HorizonsCode1
Hardware-adaptive Efficient Latency Prediction for NAS via Meta-LearningCode1
Nonrigid Reconstruction of Freehand Ultrasound without a TrackerCode1
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
Amortized Probabilistic Conditioning for Optimization, Simulation and InferenceCode1
GenSDF: Two-Stage Learning of Generalizable Signed Distance FunctionsCode1
Adaptive Subspaces for Few-Shot LearningCode1
OmniPrint: A Configurable Printed Character SynthesizerCode1
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot LearningCode1
CD-FSOD: A Benchmark for Cross-domain Few-shot Object DetectionCode1
A contrastive rule for meta-learningCode1
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective AdaptationCode1
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement LearningCode1
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
Adaptive Transfer Learning on Graph Neural NetworksCode1
How Sensitive are Meta-Learners to Dataset Imbalance?Code1
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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