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 1126–1150 of 3569 papers

TitleStatusHype
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