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 2151–2160 of 3569 papers

TitleStatusHype
Variance Reduction for Reinforcement Learning in Input-Driven Environments—0
Variational Continual Bayesian Meta-Learning—0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains—0
VIABLE: Fast Adaptation via Backpropagating Learned Loss—0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs—0
Virtual Node Tuning for Few-shot Node Classification—0
Vision transformers in domain adaptation and domain generalization: a study of robustness—0
Visual Goal-Directed Meta-Learning with Contextual Planning Networks—0
Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning—0
Visual Question Answering as a Meta Learning Task—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