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 61–70 of 3569 papers

TitleStatusHype
A contrastive rule for meta-learningCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural NetworksCode1
AirDet: Few-Shot Detection without Fine-tuning for Autonomous ExplorationCode1
BaMBNet: A Blur-aware Multi-branch Network for Defocus DeblurringCode1
A General Descent Aggregation Framework for Gradient-based Bi-level OptimizationCode1
Procedural generation of meta-reinforcement learning tasksCode1
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
Bayesian Model-Agnostic Meta-LearningCode1
Automating Outlier Detection via Meta-LearningCode1
Show:102550
← PrevPage 7 of 357Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified