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

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Showing 1151–1200 of 3569 papers

TitleStatusHype
Cross-Domain Continual Learning via CLAMPCode0
MAML MOT: Multiple Object Tracking based on Meta-Learning—0
Data-Efficient and Robust Task Selection for Meta-Learning—0
Towards Robust Physical-world Backdoor Attacks on Lane Detection—0
Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds—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
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving—0
Neural Context Flows for Meta-Learning of Dynamical SystemsCode0
FREE: Faster and Better Data-Free Meta-LearningCode0
MetaRM: Shifted Distributions Alignment via Meta-Learning—0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains—0
Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images—0
Towards Incremental Learning in Large Language Models: A Critical Review—0
MetaSD: A Unified Framework for Scalable Downscaling of Meteorological Variables in Diverse SituationsCode0
Boosting Model Resilience via Implicit Adversarial Data Augmentation—0
DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization—0
Data-Driven Performance Guarantees for Classical and Learned OptimizersCode0
Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding—0
Spectral Convolutional Conditional Neural ProcessesCode0
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models—0
Towards Robust and Interpretable EMG-based Hand Gesture Recognition using Deep Metric Meta Learning—0
A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy—0
Few-shot Name Entity Recognition on StackOverflow—0
Evaluating Fast Adaptability of Neural Networks for Brain-Computer InterfaceCode0
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model—0
Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning—0
Optimization of Lightweight Malware Detection Models For AIoT Devices—0
Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text GenerationCode0
Vision transformers in domain adaptation and domain generalization: a study of robustness—0
Domain Generalization through Meta-Learning: A Survey—0
Deep Reinforcement Learning for Traveling Purchaser Problems—0
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?—0
Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories—0
Meta Learning in Bandits within Shared Affine Subspaces—0
SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay—0
Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning—0
MetaCap: Meta-learning Priors from Multi-View Imagery for Sparse-view Human Performance Capture and Rendering—0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance EstimationCode0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification—0
A Moreau Envelope Approach for LQR Meta-Policy Estimation—0
Advancing Extrapolative Predictions of Material Properties through Learning to Learn—0
Cross-domain Multi-modal Few-shot Object Detection via Rich TextCode0
CoLLEGe: Concept Embedding Generation for Large Language Models—0
Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study—0
Learning-to-Learn the Wave Angle Estimation—0
Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent—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