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 1001–1050 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
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization—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
Distilling Symbolic Priors for Concept Learning into Neural Networks—0
FAM: fast adaptive federated meta-learning—0
Automatic learning algorithm selection for classification via convolutional neural networks—0
Automatic Forecasting via Meta-Learning—0
A Markov Decision Process Approach to Active Meta Learning—0
FORML: Learning to Reweight Data for Fairness—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
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning—0
Automatic Combination of Sample Selection Strategies for Few-Shot Learning—0
Discrete InfoMax Codes for Meta-Learning—0
Discrete Infomax Codes for Supervised Representation Learning—0
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
Discrepancy-Optimal Meta-Learning for Domain Generalization—0
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems—0
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection—0
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!—0
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization—0
Discovering Physics Laws of Dynamical Systems via Invariant Function Learning—0
Automated Grading of Students' Handwritten Graphs: A Comparison of Meta-Learning and Vision-Large Language Models—0
Alzheimer's Magnetic Resonance Imaging Classification Using Deep and Meta-Learning Models—0
Forecast with Forecasts: Diversity Matters—0
Automated Few-Shot Time Series Forecasting based on Bi-level Programming—0
Directional Domain Generalization—0
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence—0
AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation—0
Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation—0
ALT: An Automatic System for Long Tail Scenario Modeling—0
DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining—0
AutoLoss: Learning Discrete Schedule for Alternate Optimization—0
Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning—0
Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes—0
FORML: A Riemannian Hessian-free Method for Meta-learning on Stiefel Manifolds—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—0
Generating Pseudo-labels Adaptively for Few-shot Model-Agnostic Meta-Learning—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