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 2401–2425 of 3569 papers

TitleStatusHype
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Meta-learning Amidst Heterogeneity and AmbiguityCode0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Meta-Learning for Relative Density-Ratio Estimation—0
Resilient UAV Swarm Communications with Graph Convolutional Neural NetworkCode0
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-LearningCode0
Meta-learning for Matrix Factorization without Shared Rows or Columns—0
Representation based meta-learning for few-shot spoken intent recognitionCode0
Fast Training of Neural Lumigraph Representations using Meta Learning—0
Explaining the Performance of Multi-label Classification Methods with Data Set Properties—0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts—0
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation—0
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification TasksCode0
Multimodal Emergent Fake News Detection via Meta Neural Process Networks—0
BiAdam: Fast Adaptive Bilevel Optimization Methods—0
Compositional federated learning: Applications in distributionally robust averaging and meta learning—0
Task Attended Meta-Learning for Few-Shot Learning—0
Multi-Pair Text Style Transfer on Unbalanced Data—0
Transformation Invariant Few-Shot Object Detection—0
Person30K: A Dual-Meta Generalization Network for Person Re-Identification—0
Attacking Few-Shot Classifiers with Adversarial Support Poisoning—0
How Low Can We Go: Trading Memory for Error in Low-Precision TrainingCode0
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data—0
SPeCiaL: Self-Supervised Pretraining for Continual 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