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

Showing 21012125 of 3569 papers

TitleStatusHype
Fast Training of Neural Lumigraph Representations using Meta Learning0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts0
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image SegmentationCode1
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation0
Mutual-Information Based Few-Shot ClassificationCode1
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification TasksCode0
Multimodal Emergent Fake News Detection via Meta Neural Process Networks0
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth ImagesCode1
BiAdam: Fast Adaptive Bilevel Optimization Methods0
Compositional federated learning: Applications in distributionally robust averaging and meta learning0
Transfer Bayesian Meta-learning via Weighted Free Energy MinimizationCode1
Multi-Pair Text Style Transfer on Unbalanced Data0
Task Attended Meta-Learning for Few-Shot Learning0
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object SegmentationCode1
Transformation Invariant Few-Shot Object Detection0
Person30K: A Dual-Meta Generalization Network for Person Re-Identification0
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Attacking Few-Shot Classifiers with Adversarial Support Poisoning0
On Contrastive Representations of Stochastic ProcessesCode1
Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration ErrorCode1
How Low Can We Go: Trading Memory for Error in Low-Precision TrainingCode0
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data0
Transductive Few-Shot Learning: Clustering is All You Need?Code1
HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-LearningCode1
SPeCiaL: Self-Supervised Pretraining for Continual Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified