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 12111220 of 3569 papers

TitleStatusHype
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-LearningCode0
Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-LearningCode0
CMML: Contextual Modulation Meta Learning for Cold-Start RecommendationCode0
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh RecoveryCode0
Contextual Gradient Scaling for Few-Shot LearningCode0
Improving Meta-Continual Learning Representations with Representation ReplayCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
Show:102550
← PrevPage 122 of 357Next →

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