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 1251–1260 of 3569 papers

TitleStatusHype
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Deep meta-learning for the selection of accurate ultrasound based breast mass classifier—0
Toward Unsupervised Outlier Model SelectionCode1
Robust Few-shot Learning Without Using any Adversarial SamplesCode0
Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization—0
Fast Adaptive Federated Bilevel Optimization—0
Deep Multimodal Fusion for Generalizable Person Re-identificationCode0
A Meta-GNN approach to personalized seizure detection and classification—0
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport—0
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach—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