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 826–850 of 3569 papers

TitleStatusHype
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
Fast and Effective Adaptation of Facial Action Unit Detection Deep Model—0
Fast and Scalable Human Pose Estimation using mmWave Point Cloud—0
Domain Generalization: A Survey—0
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction—0
Domain-Free Adversarial Splitting for Domain Generalization—0
Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
Domain Agnostic Learning for Unbiased Authentication—0
A Meta-Learning Approach for Few-Shot (Dis)Agreement Identification in Online Discussions—0
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments—0
Fast-adapting and Privacy-preserving Federated Recommender System—0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Do What Nature Did To Us: Evolving Plastic Recurrent Neural Networks For Generalized Tasks—0
A Meta-Learning Approach for Custom Model Training—0
DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning—0
DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference—0
AutoML for Contextual Bandits—0
A Meta Learning Approach to Discerning Causal Graph Structure—0
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction—0
DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization—0
Domain Adaptation in Dialogue Systems using Transfer and Meta-Learning—0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification—0
Fast Adaptation with Kernel and Gradient based Meta Leaning—0
Fast Adaptive Anomaly Detection—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