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 1526–1550 of 3569 papers

TitleStatusHype
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
GEDI: A Graph-based End-to-end Data Imputation Framework—0
A real-time battle situation intelligent awareness system based on Meta-learning & RNN—0
Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation—0
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting—0
Digital Twin-Empowered Network Planning for Multi-Tier Computing—0
AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning—0
Contextual Stochastic Bilevel Optimization—0
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining—0
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?—0
Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification—0
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces—0
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification—0
Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation—0
Learn2Hop: Learned Optimization on Rough Landscapes—0
Directional Domain Generalization—0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations—0
Principled Acceleration of Iterative Numerical Methods Using Machine Learning—0
Function Contrastive Learning of Transferable Meta-Representations—0
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals—0
Learning Abstract Task Representations—0
Learning to Identify Physical Laws of Hamiltonian Systems via Meta-Learning—0
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery—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