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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 2401–2450 of 3569 papers

TitleStatusHype
The Gaussian Neural ProcessCode1
Evolving Reinforcement Learning AlgorithmsCode1
Shallow Bayesian Meta Learning for Real-World Few-Shot RecognitionCode1
Few-Shot Learning with Class ImbalanceCode1
Curriculum-Meta Learning for Order-Robust Continual Relation ExtractionCode1
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding—0
MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal ControlCode1
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
Meta-Learning Conjugate Priors for Few-Shot Bayesian Optimization—0
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments—0
Z-Score Normalization, Hubness, and Few-Shot Learning—0
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition—0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack—0
Task-Aware Part Mining Network for Few-Shot Learning—0
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-LearningCode0
Towards Robust Graph Neural Networks against Label Noise—0
To Learn Effective Features: Understanding the Task-Specific Adaptation of MAML—0
Task Calibration for Distributional Uncertainty in Few-Shot Classification—0
Robust Meta-learning with Noise via Eigen-Reptile—0
Semantic Inference Network for Few-shot Streaming Label Learning—0
Auto-view contrastive learning for few-shot image recognition—0
A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning—0
Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory—0
Meta-Learned Confidence for Transductive Few-shot Learning—0
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-LearningCode1
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot LearningCode1
Unsupervised Task Clustering for Multi-Task Reinforcement LearningCode0
Few-Round Learning for Federated Learning—0
Distribution Embedding Network for Meta-Learning with Variable-Length Input—0
MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning—0
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains—0
Learning to Identify Physical Laws of Hamiltonian Systems via Meta-Learning—0
ERMAS: Learning Policies Robust to Reality Gaps in Multi-Agent Simulations—0
cross-modal knowledge enhancement mechanism for few-shot learning—0
Domain-Free Adversarial Splitting for Domain Generalization—0
Navigating the Trade-Off between Learning Efficacy and Processing Efficiency in Deep Neural Networks—0
Towards Learning to Remember in Meta Learning of Sequential Domains—0
TaskSet: A Dataset of Optimization TasksCode0
Meta-Learning with Implicit Processes—0
Adaptive Self-training for Neural Sequence Labeling with Few Labels—0
Bayesian Meta-Learning for Few-Shot 3D Shape Completion—0
Optimal allocation of data across training tasks in meta-learning—0
Meta Attention Networks: Meta-Learning Attention to Modulate Information Between Recurrent Independent Mechanisms—0
Variable-Shot Adaptation for Incremental Meta-Learning—0
Uncertain Out-of-Domain Generalization—0
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior—0
Model agnostic meta-learning on trees—0
Meta-Learning in Reproducing Kernel Hilbert Space—0
Dataset Meta-Learning from Kernel-Ridge Regression—0
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks—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