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 2711–2720 of 3569 papers

TitleStatusHype
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning—0
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach—0
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction—0
MATE: Plugging in Model Awareness to Task Embedding for Meta LearningCode0
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification—0
Adversarial Attacks on Deep Graph Matching—0
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot TasksCode0
Meta learning to classify intent and slot labels with noisy few shot examples—0
Is Support Set Diversity Necessary for Meta-Learning?—0
Connecting Context-specific Adaptation in Humans to Meta-learning—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