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 1026–1050 of 3569 papers

TitleStatusHype
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
Show:102550
← PrevPage 42 of 143Next →
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