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 1331–1340 of 3569 papers

TitleStatusHype
Informed Meta-Learning—0
Hierarchical Expert Networks for Meta-Learning—0
Assessing two novel distance-based loss functions for few-shot image classification—0
Hierarchical Meta Learning—0
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives—0
High-Order Deep Meta-Learning with Category-Theoretic Interpretation—0
Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning—0
Home Appliance Review Research Via Adversarial Reptile—0
Generalized Visual Quality Assessment of GAN-Generated Face Images—0
Generalized Reinforcement Meta Learning for Few-Shot Optimization—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