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 1451–1500 of 3569 papers

TitleStatusHype
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
Information-theoretic Analysis of Test Data Sensitivity in Uncertainty—0
Information-Theoretic Foundations for Machine Learning—0
Information-Theoretic Generalization Bounds for Meta-Learning and Applications—0
Generalizable Person Re-Identification by Domain-Invariant Mapping Network—0
Informed Meta-Learning—0
In-Loop Meta-Learning with Gradient-Alignment Reward—0
Deep neural network ensemble by data augmentation and bagging for skin lesion classification—0
Continuous Learning in a Hierarchical Multiscale Neural Network—0
InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning—0
INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy—0
Integrated and Adaptive Guidance and Control for Endoatmospheric Missiles via Reinforcement Learning—0
Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?—0
Integrated Image-Text Based on Semi-supervised Learning for Small Sample Instance Segmentation—0
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks—0
Intelligent Travel Activity Monitoring: Generalized Distributed Acoustic Sensing Approaches—0
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks—0
Interactive Graph Convolutional Filtering—0
Interactive singing melody extraction based on active adaptation—0
Interpretable Automated Machine Learning in Maana(TM) Knowledge Platform—0
Learning Tensor Representations for Meta-Learning—0
Generalizable Neural Fields as Partially Observed Neural Processes—0
Interpretable Meta-Learning of Physical Systems—0
Attribute Propagation Network for Graph Zero-shot Learning—0
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online—0
Generalizable Heuristic Generation Through Large Language Models with Meta-Optimization—0
Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning—0
Introducing Symmetries to Black Box Meta Reinforcement Learning—0
Continual Quality Estimation with Online Bayesian Meta-Learning—0
Invariant Meta Learning for Out-of-Distribution Generalization—0
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning—0
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition—0
Learning to Actively Learn: A Robust Approach—0
Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization—0
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks—0
Investigating Relative Performance of Transfer and Meta Learning—0
Learning to Adapt to Low-Resource Paraphrase Generation—0
Continual learning under domain transfer with sparse synaptic bursting—0
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning—0
Is Fast Adaptation All You Need?—0
Continual Few-Shot Learning with Adversarial Class Storage—0
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
Is Pre-training Truly Better Than Meta-Learning?—0
Is Support Set Diversity Necessary for Meta-Learning?—0
Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?—0
GEDI: A Graph-based End-to-end Data Imputation Framework—0
Investigating Active Learning and Meta-Learning for Iterative Peptide Design—0
AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All—0
A real-time battle situation intelligent awareness system based on Meta-learning & RNN—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