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 801–810 of 3569 papers

TitleStatusHype
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning—0
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding—0
Subspace Adaptation Prior for Few-Shot LearningCode0
Learn From Model Beyond Fine-Tuning: A SurveyCode1
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning—0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters—0
Neural Relational Inference with Fast Modular Meta-learningCode1
Self-Supervised Dataset Distillation for Transfer LearningCode1
Federated Multi-Level Optimization over Decentralized Networks—0
Understanding Transfer Learning and Gradient-Based Meta-Learning TechniquesCode0
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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