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 1011–1020 of 3569 papers

TitleStatusHype
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation—0
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction—0
Distributed Estimation by Two Agents with Different Feature Spaces—0
FAM: fast adaptive federated meta-learning—0
Automatic Learning to Detect Concept Drift—0
Amazon SageMaker Autopilot: a white box AutoML solution at scale—0
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization—0
Distilling Symbolic Priors for Concept Learning into Neural Networks—0
Fast Adaptation with Kernel and Gradient based Meta Leaning—0
Automatic learning algorithm selection for classification via convolutional neural networks—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