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 2951–3000 of 3569 papers

TitleStatusHype
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning—0
Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning—0
Generalizable Heuristic Generation Through Large Language Models with Meta-Optimization—0
Generalizable Neural Fields as Partially Observed Neural Processes—0
Generalizable Person Re-Identification by Domain-Invariant Mapping Network—0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
Generalizable speech deepfake detection via meta-learned LoRA—0
Generalization in Neural Networks: A Broad Survey—0
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks—0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—0
Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss—0
Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning—0
Generalized Reinforcement Meta Learning for Few-Shot Optimization—0
Generalized Visual Quality Assessment of GAN-Generated Face Images—0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts—0
Generating meta-learning tasks to evolve parametric loss for classification learning—0
Generating Personalized Dialogue via Multi-Task Meta-Learning—0
Generating Pseudo-labels Adaptively for Few-shot Model-Agnostic Meta-Learning—0
Generative Conversational Networks—0
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction—0
Generative Neural Fields by Mixtures of Neural Implicit Functions—0
Generative Semi-supervised Learning with Meta-Optimized Synthetic Samples—0
GenMetaLoc: Learning to Learn Environment-Aware Fingerprint Generation for Sample Efficient Wireless Localization—0
Geometric Meta-Learning via Coupled Ricci Flow: Unifying Knowledge Representation and Quantum Entanglement—0
Geometry Aware Meta-Learning Neural Network for Joint Phase and Precoder Optimization in RIS—0
When Does MAML Objective Have Benign Landscape?—0
Global Perception Based Autoregressive Neural Processes—0
G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems—0
Gradient Agreement as an Optimization Objective for Meta-Learning—0
Gradient-Based Meta Learning for Uplink RSMA with Beyond Diagonal RIS—0
Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning—0
Gradient-based Meta-solving and Its Applications to Iterative Methods for Solving Differential Equations—0
Gradient-EM Bayesian Meta-learning—0
Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models—0
Grad-Instructor: Universal Backpropagation with Explainable Evaluation Neural Networks for Meta-learning and AutoML—0
GradMix: Multi-source Transfer across Domains and Tasks—0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Grammatical Error Correction Using Feature Selection and Confidence Tuning—0
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs—0
Graph Mining under Data scarcity—0
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression—0
Graph neural network initialisation of quantum approximate optimisation—0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
Ground-Truth Free Meta-Learning for Deep Compressive Sampling—0
Group Equivariant Conditional Neural Processes—0
Guided Evolutionary Strategies: Escaping the curse of dimensionality in random search—0
Guided Meta-Policy Search—0
Guided Variational Autoencoder for Disentanglement Learning—0
Heterosynaptic Circuits Are Universal Gradient Machines—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