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 391400 of 3569 papers

TitleStatusHype
Cross-Domain Few-Shot Classification via Adversarial Task AugmentationCode1
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph CompletionCode1
X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question AnsweringCode1
Editing Factual Knowledge in Language ModelsCode1
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual LearningCode1
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature AlignmentCode1
Generalizable No-Reference Image Quality Assessment via Deep Meta-learningCode1
Learning Normal Dynamics in Videos with Meta Prototype NetworkCode1
How Sensitive are Meta-Learners to Dataset Imbalance?Code1
Direct Differentiable Augmentation SearchCode1
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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