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 2426–2450 of 3569 papers

TitleStatusHype
Attention-based Few-Shot Person Re-identification Using Meta Learning—0
Attentive Feature Reuse for Multi Task Meta learning—0
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification—0
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification—0
Attribute Propagation Network for Graph Zero-shot Learning—0
Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning—0
Augmentation Learning for Semi-Supervised Classification—0
Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis—0
Augmenting Supervised Learning by Meta-learning Unsupervised Local Rules—0
A Unified Framework with Meta-dropout for Few-shot Learning—0
A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI—0
Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning—0
AutoLoss: Learning Discrete Schedule for Alternate Optimization—0
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence—0
Automated Few-Shot Time Series Forecasting based on Bi-level Programming—0
Automated Grading of Students' Handwritten Graphs: A Comparison of Meta-Learning and Vision-Large Language Models—0
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems—0
Automatic Combination of Sample Selection Strategies for Few-Shot Learning—0
Automatic Forecasting via Meta-Learning—0
Automatic learning algorithm selection for classification via convolutional neural networks—0
Automatic Learning to Detect Concept Drift—0
Automatic low-bit hybrid quantization of neural networks through meta learning—0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning—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