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 301–325 of 3569 papers

TitleStatusHype
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
Unlocking Transfer Learning for Open-World Few-Shot Recognition—0
Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment—0
Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model GeneralizationCode0
T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR ImagingCode0
Neuromodulated Meta-LearningCode0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination GamesCode0
Towards 3D Semantic Scene Completion for Autonomous Driving: A Meta-Learning Framework Empowered by Deformable Large-Kernel Attention and Mamba Model—0
Towards more efficient agricultural practices via transformer-based crop type classification—0
Learning Where to Edit Vision TransformersCode0
Generalizable and Robust Spectral Method for Multi-view Representation LearningCode0
Transferable Sequential Recommendation via Vector Quantized Meta Learning—0
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain SetupsCode0
Teaching Models to Improve on Tape—0
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning—0
Transfer Learning for Finetuning Large Language Models—0
MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning—0
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization—0
Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement LearningCode1
Fast Adaptation with Kernel and Gradient based Meta Leaning—0
Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-OptimizationCode2
First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling—0
Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learning—0
Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta LearningCode0
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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