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 761770 of 3569 papers

TitleStatusHype
MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept AcquisitionCode0
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval0
Investigating Relative Performance of Transfer and Meta Learning0
STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction0
Meta Learning for Multi-View Visuomotor Systems0
Meta-Learning Strategies through Value Maximization in Neural Networks0
Adaptive Meta-Learning-Based KKL Observer Design for Nonlinear Dynamical SystemsCode0
Generative Neural Fields by Mixtures of Neural Implicit Functions0
A Survey on Knowledge Editing of Neural Networks0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach0
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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