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 451–460 of 3569 papers

TitleStatusHype
Adaptive Cascading Network for Continual Test-Time AdaptationCode0
Information-Theoretic Foundations for Machine Learning—0
An Evaluation of Continual Learning for Advanced Node Semiconductor Defect Inspection—0
Siamese Transformer Networks for Few-shot Image Classification—0
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments—0
Efficient In-Context Medical Segmentation with Meta-driven Visual Prompt Selection—0
Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse ProblemsCode2
Learning to Unlearn for Robust Machine Unlearning—0
Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions—0
A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification—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