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 481490 of 3569 papers

TitleStatusHype
Modular meta-learningCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot SegmentationCode1
MT3: Meta Test-Time Training for Self-Supervised Test-Time AdaptionCode1
Multi-Modal Few-Shot Temporal Action DetectionCode1
Multimodal Model-Agnostic Meta-Learning via Task-Aware ModulationCode1
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-LearningCode1
Cross-Domain Few-Shot Semantic SegmentationCode1
Amortized Probabilistic Conditioning for Optimization, Simulation and InferenceCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
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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