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 1601–1650 of 3569 papers

TitleStatusHype
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise—0
Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images—0
A Meta-Learning Approach to Predicting Performance and Data Requirements—0
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness—0
Bayesian Active Meta-Learning for Black-Box Optimization—0
Dynamic Channel Access via Meta-Reinforcement Learning—0
Dynamic backdoor attacks against federated learning—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Adaptive Hierarchical Hyper-gradient Descent—0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Dual Path Structural Contrastive Embeddings for Learning Novel Objects—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
Dual Meta-Learning with Longitudinally Consistent Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan—0
A Meta-Learning Approach to One-Step Active Learning—0
Dual-Level Viewpoint-Learning for Cross-Domain Vehicle Re-Identification—0
A Meta Learning Approach to Grammatical Error Correction—0
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing—0
A Comprehensive Review of Few-shot Action Recognition—0
DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization—0
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction—0
Backdoor Attacks on Federated Meta-Learning—0
DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference—0
DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning—0
Auxiliary task discovery through generate-and-test—0
Do What Nature Did To Us: Evolving Plastic Recurrent Neural Networks For Generalized Tasks—0
Auxiliary learning induced graph convolutional networks—0
A Meta Learning Approach to Discerning Causal Graph Structure—0
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments—0
Auto-view contrastive learning for few-shot image recognition—0
Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration—0
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments—0
A Comprehensive Overview and Survey of Recent Advances in Meta-Learning—0
A behavioural transformer for effective collaboration between a robot and a non-stationary human—0
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition—0
Domain-Generalized Textured Surface Anomaly Detection—0
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning—0
Domain Generalization through Meta-Learning: A Survey—0
Learning to Generalize to Unseen Tasks with Bilevel Optimization—0
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation—0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters—0
A Meta-Learning Approach for Medical Image Registration—0
Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition—0
Domain Generalization: A Survey—0
Domain-Free Adversarial Splitting for Domain Generalization—0
Domain Agnostic Learning for Unbiased Authentication—0
A Meta-Learning Approach for Few-Shot (Dis)Agreement Identification in Online Discussions—0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Learning to Cope with Adversarial Attacks—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