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 281290 of 3569 papers

TitleStatusHype
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Covariate Distribution Aware Meta-learningCode1
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot LearningCode1
Cross-Market Product RecommendationCode1
Adversarial Feature Augmentation for Cross-domain Few-shot ClassificationCode1
Curriculum-Meta Learning for Order-Robust Continual Relation ExtractionCode1
Are Deep Neural Networks SMARTer than Second Graders?Code1
BOML: A Modularized Bilevel Optimization Library in Python for Meta LearningCode1
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-LearningCode1
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
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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