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 351375 of 3569 papers

TitleStatusHype
Procedural generation of meta-reinforcement learning tasksCode1
A Closer Look at Few-Shot Video Classification: A New Baseline and BenchmarkCode1
A Large Scale Search Dataset for Unbiased Learning to RankCode1
FewSAR: A Few-shot SAR Image Classification BenchmarkCode1
Federated Reconstruction: Partially Local Federated LearningCode1
Learning a Formula of Interpretability to Learn Interpretable FormulasCode1
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
Attentional-Biased Stochastic Gradient DescentCode1
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate PredictionCode1
Learning Meta Face Recognition in Unseen DomainsCode1
Few-shot Action Recognition with Prototype-centered Attentive LearningCode1
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object DetectionCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
Learning Symbolic Model-Agnostic Loss Functions via Meta-LearningCode1
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object SegmentationCode1
Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object DetectionCode1
Learning to Compare: Relation Network for Few-Shot LearningCode1
Learning to Continually LearnCode1
Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype EnhancementCode1
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video RecognitionCode1
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link PredictionCode1
Learning to Filter: Siamese Relation Network for Robust TrackingCode1
Automating Outlier Detection via Meta-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