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 221230 of 3569 papers

TitleStatusHype
Fine-grained Recognition with Learnable Semantic Data AugmentationCode1
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
Covariate Distribution Aware Meta-learningCode1
Adversarial Feature Augmentation for Cross-domain Few-shot ClassificationCode1
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in ContextCode1
FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real UsersCode1
Generalizable Black-Box Adversarial Attack with Meta LearningCode1
Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain GeneralizationCode1
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural NetworksCode1
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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