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 526–550 of 3569 papers

TitleStatusHype
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2Code2
Perturbing the Gradient for Alleviating Meta OverfittingCode0
Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed AdaptationCode1
Alzheimer's Magnetic Resonance Imaging Classification Using Deep and Meta-Learning Models—0
Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement LearningCode0
Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning—0
Meta Reinforcement Learning for Resource Allocation in Multi-Antenna UAV Network with Rate Splitting Multiple Access—0
On the Performance of Unmanned Aerial Vehicles with MIMO VLC—0
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks—0
Beyond Traditional Single Object Tracking: A Survey—0
Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications—0
On-device Online Learning and Semantic Management of TinyML SystemsCode0
MAML MOT: Multiple Object Tracking based on Meta-Learning—0
Cross-Domain Continual Learning via CLAMPCode0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
Data-Efficient and Robust Task Selection for Meta-Learning—0
Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds—0
Towards Robust Physical-world Backdoor Attacks on Lane Detection—0
Dual-Level Viewpoint-Learning for Cross-Domain Vehicle Re-Identification—0
Meta-Learning for Efficient Fine-Tuning of Large Language ModelsCode0
Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial TransferabilityCode0
Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning—0
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving—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