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 561570 of 3569 papers

TitleStatusHype
Few-shot Text Classification with Distributional SignaturesCode1
Induction Networks for Few-Shot Text ClassificationCode1
Direct Differentiable Augmentation SearchCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
Fine-grained Recognition with Learnable Semantic Data AugmentationCode1
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object SegmentationCode1
From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication SystemsCode1
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in ContextCode1
Concept Learners for Few-Shot 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