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 21012110 of 3569 papers

TitleStatusHype
Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images0
Transformative Machine Learning0
Transformer-Based Contrastive Meta-Learning For Low-Resource Generalizable Activity Recognition0
Transformers for Supervised Online Continual Learning0
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning0
Towards Incremental Learning in Large Language Models: A Critical Review0
TSRating: Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment0
UBMF: Uncertainty-Aware Bayesian Meta-Learning Framework for Fault Diagnosis with Imbalanced Industrial Data0
u-cf2vec: Representation Learning for Personalized Algorithm Selection in Recommender Systems0
UFO-BLO: Unbiased First-Order Bilevel Optimization0
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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