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

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Showing 211–220 of 3569 papers

TitleStatusHype
Learning to Learn Weight Generation via Local Consistency Diffusion—0
qNBO: quasi-Newton Meets Bilevel Optimization—0
Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation ModelsCode0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network—0
Meta-learning of shared linear representations beyond well-specified linear regression—0
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces—0
Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information—0
ASAP: Learning Generalizable Online Bin Packing via Adaptive Selection After Pruning—0
In-Context Meta LoRA Generation—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