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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2126–2150 of 3569 papers

TitleStatusHype
Semi-Stacking for Semi-supervised Sentiment Classification—0
Semi-Supervised Few-Shot Intent Classification and Slot Filling—0
Semi-Supervised Few-Shot Learning with a Controlled Degree of Task-Adaptive Conditioning—0
Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining—0
Semi Supervised Meta Learning for Spatiotemporal Learning—0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification—0
Semi-Supervised Variational Inference over Nonlinear Channels—0
SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathology—0
Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification—0
Sequential Learning for Domain Generalization—0
Set2Model Networks: Learning Discriminatively To Learn Generative Models—0
Set a Thief to Catch a Thief: Combating Label Noise through Noisy Meta Learning—0
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
Set-to-Sequence Methods in Machine Learning: a Review—0
SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification—0
Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review—0
Short-Term Stock Price-Trend Prediction Using Meta-Learning—0
Short-term Traffic Prediction with Deep Neural Networks: A Survey—0
Should Models Be Accurate?—0
Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]—0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—0
Siamese Transformer Networks for Few-shot Image Classification—0
Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus—0
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition—0
SimCompass: Using Deep Learning Word Embeddings to Assess Cross-level Similarity—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