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

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Showing 2801–2850 of 3569 papers

TitleStatusHype
Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification—0
Explaining the Performance of Multi-label Classification Methods with Data Set Properties—0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling—0
Exploring Domain Shift in Extractive Text Summarization—0
Exploring Frequency Adversarial Attacks for Face Forgery Detection—0
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study—0
Exploring intra-task relations to improve meta-learning algorithms—0
Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training—0
Extension of Transformational Machine Learning: Classification Problems—0
Extracting more from boosted decision trees: A high energy physics case study—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
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations—0
FAM: fast adaptive federated meta-learning—0
Fast Adaptation for Human Pose Estimation via Meta-Optimization—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
Fast Adaptive Federated Bilevel Optimization—0
Fast and Effective Adaptation of Facial Action Unit Detection Deep Model—0
Fast and Scalable Human Pose Estimation using mmWave Point Cloud—0
Fast and Slow Learning of Recurrent Independent Mechanisms—0
Fast Data-Driven Adaptation of Radar Detection via Meta-Learning—0
Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization—0
Faster Optimization-Based Meta-Learning Adaptation Phase—0
Fast Image Classification by Boosting Fuzzy Classifiers—0
Fast Medical Shape Reconstruction via Meta-learned Implicit Neural Representations—0
Fast On-Device Adaptation for Spiking Neural Networks via Online-Within-Online Meta-Learning—0
Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks—0
Fast Rate Bounds for Multi-Task and Meta-Learning with Different Sample Sizes—0
Fast & Slow Learning: Incorporating Synthetic Gradients in Neural Memory Controllers—0
Fast Training Method for Stochastic Compositional Optimization Problems—0
Fast Training Method for Stochastic Compositional Optimization Problems—0
Fast Training of Neural Lumigraph Representations using Meta Learning—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
Feature-context driven Federated Meta-Learning for Rare Disease Prediction—0
Federated Learning and Meta Learning: Approaches, Applications, and Directions—0
Federated Conditional Stochastic Optimization—0
A Benchmark for Federated Hetero-Task 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