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 376–400 of 3569 papers

TitleStatusHype
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain SchedulerCode0
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification—0
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition—0
Hierarchical end-to-end autonomous navigation through few-shot waypoint detection—0
From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding—0
ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuningCode0
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification—0
Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation—0
Geometry Aware Meta-Learning Neural Network for Joint Phase and Precoder Optimization in RIS—0
ADIOS: Antibody Development via Opponent ShapingCode0
SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition—0
Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction—0
AALF: Almost Always Linear ForecastingCode0
Online Nonconvex Bilevel Optimization with Bregman Divergences—0
Rethinking Meta-Learning from a Learning Lens—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs—0
Fast Medical Shape Reconstruction via Meta-learned Implicit Neural Representations—0
Spoofing-Aware Speaker Verification Robust Against Domain and Channel Mismatches—0
Few-Shot Learning: Expanding ID Cards Presentation Attack Detection to Unknown ID Countries—0
Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AICode0
Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysis—0
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
Boosting CLIP Adaptation for Image Quality Assessment via Meta-Prompt Learning and Gradient Regularization—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