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 2851–2875 of 3569 papers

TitleStatusHype
Neural Complexity MeasuresCode0
Data-driven Meta-set Based Fine-Grained Visual ClassificationCode0
Improving End-to-End Speech-to-Intent Classification with Reptile—0
A Neural-Symbolic Framework for Mental Simulation—0
Meta-DRN: Meta-Learning for 1-Shot Image Segmentation—0
Adaptive Variance Based Label Distribution Learning For Facial Age Estimation—0
Speech-driven Facial Animation using Cascaded GANs for Learning of Motion and Texture—0
Incremental Few-Shot Meta-Learning via Indirect Discriminant Alignment—0
Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records—0
Targeted Data-driven Regularization for Out-of-Distribution GeneralizationCode0
Learning to Learn to Compress—0
Learning from Few Samples: A Survey—0
Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction—0
Meta-Learning with Context-Agnostic InitialisationsCode0
Universality of Gradient Descent Neural Network Training—0
Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning—0
MetAL: Active Semi-Supervised Learning on Graphs via Meta LearningCode0
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning ApproachCode0
Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks—0
Meta-learning for Few-shot Natural Language Processing: A Survey—0
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search—0
A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems—0
Adaptive Task Sampling for Meta-Learning—0
Contextualizing Enhances Gradient Based Meta LearningCode0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—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