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 2701–2750 of 3569 papers

TitleStatusHype
Meta-Learning with Context-Agnostic InitialisationsCode0
La-MAML: Look-ahead Meta Learning for Continual LearningCode1
Universality of Gradient Descent Neural Network Training—0
Improving Generalization in Meta-learning via Task AugmentationCode1
Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning—0
Few-Shot Object Detection and Viewpoint Estimation for Objects in the WildCode1
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
Fuzzy Graph Neural Network for Few-Shot LearningCode1
Meta-learning for Few-shot Natural Language Processing: A Survey—0
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search—0
Contextualizing Enhances Gradient Based Meta LearningCode0
A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems—0
Probabilistic Active Meta-LearningCode1
Adaptive Task Sampling for Meta-Learning—0
Discovering Reinforcement Learning AlgorithmsCode1
Collision Avoidance Robotics Via Meta-Learning (CARML)Code0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous GraphsCode1
Learning to Learn with Variational Information Bottleneck for Domain Generalization—0
How to trust unlabeled data? Instance Credibility Inference for Few-Shot LearningCode1
Few-shot Scene-adaptive Anomaly DetectionCode1
Gradient-based Hyperparameter Optimization Over Long HorizonsCode1
Concept Learners for Few-Shot LearningCode1
Top-Related Meta-Learning Method for Few-Shot Object DetectionCode0
Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification—0
Towards Cross-Granularity Few-Shot Learning: Coarse-to-Fine Pseudo-Labeling with Visual-Semantic Meta-Embedding—0
Submodular Meta-LearningCode0
Online Parameter-Free Learning of Multiple Low Variance TasksCode0
Meta Soft Label Generation for Noisy LabelsCode1
Meta-Learning Requires Meta-Augmentation—0
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification—0
Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing—0
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant NetworkCode0
Semi-Supervised Learning with Meta-GradientCode1
Auto-Sklearn 2.0: Hands-free AutoML via Meta-LearningCode3
Few-Shot One-Class Classification via Meta-LearningCode1
Meta-Learning with Network Pruning—0
Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network—0
Meta-active Learning in Probabilistically-Safe Optimization—0
MAMO: Memory-Augmented Meta-Optimization for Cold-start RecommendationCode1
Meta-Learning Symmetries by ReparameterizationCode2
Covariate Distribution Aware Meta-learningCode1
Meta-Learning Divergences of Variational Inference—0
Meta Learning for Causal Direction—0
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network EmbeddingCode1
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
Few-shot Relation Extraction via Bayesian Meta-learning on Relation GraphsCode1
Meta-Semi: A Meta-learning Approach for Semi-supervised Learning—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