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 426450 of 3569 papers

TitleStatusHype
Exploring Task Difficulty for Few-Shot Relation ExtractionCode1
BaMBNet: A Blur-aware Multi-branch Network for Defocus DeblurringCode1
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot LearningCode1
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
Meta-Learning to Compositionally GeneralizeCode1
Adaptive Multi-Teacher Knowledge Distillation with Meta-LearningCode1
Fast and Efficient Local Search for Genetic Programming Based Loss Function LearningCode1
Fast Context Adaptation via Meta-LearningCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated PriorsCode1
FewSAR: A Few-shot SAR Image Classification BenchmarkCode1
Federated Reconstruction: Partially Local Federated LearningCode1
Meta-learning with an Adaptive Task SchedulerCode1
Meta-Learning with Implicit GradientsCode1
Meta-Learning with Latent Embedding OptimizationCode1
Bayesian Model-Agnostic Meta-LearningCode1
Meta-Learning with Sparse Experience Replay for Lifelong Language LearningCode1
Few-Shot Class-Incremental Learning by Sampling Multi-Phase TasksCode1
MetaMask: Revisiting Dimensional Confounder for Self-Supervised LearningCode1
Few-shot Classification via Adaptive AttentionCode1
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot LearningCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
Few-Shot Graph Learning for Molecular Property PredictionCode1
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in ContextCode1
Show:102550
← PrevPage 18 of 143Next →

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