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 1001–1025 of 3569 papers

TitleStatusHype
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning—0
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition—0
Face-Focused Cross-Stream Network for Deception Detection in Videos—0
FaceSpoof Buster: a Presentation Attack Detector Based on Intrinsic Image Properties and Deep Learning—0
FAF: A Feature-Adaptive Framework for Few-Shot Time Series Forecasting—0
FADE: Towards Fairness-aware Augmentation for Domain Generalization via Classifier-Guided Score-based Diffusion Models—0
Fair Meta-Learning For Few-Shot Classification—0
Fair Meta-Learning: Learning How to Learn Fairly—0
Fairness-Aware Meta-Learning via Nash Bargaining—0
Fairness-Aware Online Meta-learning—0
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation—0
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction—0
Distributed Evolution Strategies Using TPUs for Meta-Learning—0
FAM: fast adaptive federated meta-learning—0
Distributed Estimation by Two Agents with Different Feature Spaces—0
Automatic Learning to Detect Concept Drift—0
Amazon SageMaker Autopilot: a white box AutoML solution at scale—0
Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets—0
Fast Adaptation with Kernel and Gradient based Meta Leaning—0
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction—0
Fast-adapting and Privacy-preserving Federated Recommender System—0
Fast Adaptive Anomaly Detection—0
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization—0
Distilling Symbolic Priors for Concept Learning into Neural Networks—0
Automatic learning algorithm selection for classification via convolutional neural networks—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