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 1101–1150 of 3569 papers

TitleStatusHype
Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning—0
Attribute Propagation Network for Graph Zero-shot Learning—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
All in One: Multi-task Prompting for Graph Neural Networks—0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?—0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
Deep Reinforcement Learning for Traveling Purchaser Problems—0
Deep Reinforcement Learning, a textbook—0
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification—0
Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL—0
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility—0
Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification—0
Deep Neural Networks based Meta-Learning for Network Intrusion Detection—0
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification—0
Deep neural network ensemble by data augmentation and bagging for skin lesion classification—0
Attentive Feature Reuse for Multi Task Meta learning—0
Algorithm Selection Framework for Cyber Attack Detection—0
A Closer Look at Prototype Classifier for Few-shot Image Classification—0
Deep Metric Learning via Adaptive Learnable Assessment—0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments—0
Deep Meta-Learning: Learning to Learn in the Concept Space—0
Deep Meta-learning in Recommendation Systems: A Survey—0
Attention-based Few-Shot Person Re-identification Using Meta Learning—0
Algorithm Design for Online Meta-Learning with Task Boundary Detection—0
Deep meta-learning for the selection of accurate ultrasound based breast mass classifier—0
Deep Meta Learning for Real-Time Target-Aware Visual Tracking—0
ALCN: Meta-Learning for Contrast Normalization Applied to Robust 3D Pose Estimation—0
Meta-Learning Mean Functions for Gaussian Processes—0
Deep Learning with Label Noise: A Hierarchical Approach—0
Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints—0
Adaptive Fairness-Aware Online Meta-Learning for Changing Environments—0
3D Meta-Registration: Meta-learning 3D Point Cloud Registration Functions—0
Deep Learning from Small Amount of Medical Data with Noisy Labels: A Meta-Learning Approach—0
Attacking Few-Shot Classifiers with Adversarial Support Poisoning—0
Deep Knowledge Based Agent: Learning to do tasks by self-thinking about imaginary worlds—0
GradMix: Multi-source Transfer across Domains and Tasks—0
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning—0
Deep domain adaptation for polyphonic melody extraction—0
Attacking Few-Shot Classifiers with Adversarial Support Sets—0
A Thorough Review on Recent Deep Learning Methodologies for Image Captioning—0
A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning—0
Adaptive Ensemble Learning with Confidence Bounds—0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
When MAML Can Adapt Fast and How to Assist When It Cannot—0
Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning—0
A Theoretical Analysis of the Number of Shots in Few-Shot Learning—0
Decoupled Pronunciation and Prosody Modeling in Meta-Learning-Based Multilingual Speech Synthesis—0
Decoupled DETR For Few-shot Object Detection—0
A Systematic Review of Few-Shot Learning in Medical Imaging—0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior—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