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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2051–2060 of 3569 papers

TitleStatusHype
Towards a population-informed approach to the definition of data-driven models for structural dynamics—0
Towards Automated Error Analysis: Learning to Characterize Errors—0
Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition—0
Towards explainable meta-learning—0
Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders—0
Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification—0
Towards Efficient and Effective Alignment of Large Language Models—0
Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks—0
Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts—0
Towards General and Efficient Online Tuning for Spark—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified