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 2801–2825 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
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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