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 2441–2450 of 3569 papers

TitleStatusHype
A Meta Learning Approach to Discerning Causal Graph Structure—0
DAMSL: Domain Agnostic Meta Score-based LearningCode0
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation—0
Meta-learning for downstream aware and agnostic pretraining—0
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition—0
Meta-Learning with Variational Semantic Memory for Word Sense DisambiguationCode0
Minimax and Neyman-Pearson Meta-Learning for Outlier LanguagesCode0
DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference—0
Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning—0
Modality-specific Distillation—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