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 801–825 of 3569 papers

TitleStatusHype
A Meta Learning Approach to Discerning Causal Graph Structure—0
Auxiliary task discovery through generate-and-test—0
Auxiliary learning induced graph convolutional networks—0
A behavioural transformer for effective collaboration between a robot and a non-stationary human—0
Test like you Train in Implicit Deep Learning—0
Auto-view contrastive learning for few-shot image recognition—0
A Comprehensive Overview and Survey of Recent Advances in Meta-Learning—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
Fairness-Aware Meta-Learning via Nash Bargaining—0
Fairness-Aware Online Meta-learning—0
Fast-adapting and Privacy-preserving Federated Recommender System—0
Face-Focused Cross-Stream Network for Deception Detection in Videos—0
A Meta-Learning Approach for Medical Image Registration—0
FaceSpoof Buster: a Presentation Attack Detector Based on Intrinsic Image Properties and Deep Learning—0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters—0
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition—0
FAF: A Feature-Adaptive Framework for Few-Shot Time Series Forecasting—0
Domain Generalization: A Survey—0
Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training—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 Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation—0
Extension of Transformational Machine Learning: Classification Problems—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