Image de couverture de livre

BayPrAnoMeta: Bayesian Proto-MAML for Few-Shot Industrial Image Anomaly Detection

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Sarkar, Soham, Sen, Tanmay, Banerjee, Sayantan
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

FSL-BDP: Federated Survival Learning with Bayesian Differential Privacy for Credit Risk Modeling
par: Amed, Sultan, et autres
Publié: (2026)

HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks
par: Przewięźlikowski, M., et autres
Publié: (2022)

Meta-Learning Based Few-Shot Graph-Level Anomaly Detection
par: Li, Liting, et autres
Publié: (2025)

MAML-KT: Addressing Cold Start Problem in Knowledge Tracing for New Students via Few-Shot Model-Agnostic Meta Learning
par: Bhattacharjee, Indronil, et autres
Publié: (2026)

Anomalous Samples for Few-Shot Anomaly Detection
par: Abdali, Aymane, et autres
Publié: (2025)

MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection
par: Xu, Xiongxiao, et autres
Publié: (2023)

ProtoTopic: Prototypical Network for Few-Shot Medical Topic Modeling
par: Licht, Martin, et autres
Publié: (2025)

Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
par: Kim, Soopil, et autres
Publié: (2023)

From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection
par: Liu, Yixin, et autres
Publié: (2026)

Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification
par: Ke, Tianjun, et autres
Publié: (2023)

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation
par: Mutlu, Abdulvahap, et autres
Publié: (2025)

DP-FedSOFIM: Differentially Private Federated Stochastic Optimization using Regularized Fisher Information Matrix
par: Nair, Sidhant, et autres
Publié: (2026)

Directed-MAML: Meta Reinforcement Learning Algorithm with Task-directed Approximation
par: Zhang, Yang, et autres
Publié: (2025)

Compositional Program Generation for Few-Shot Systematic Generalization
par: Klinger, Tim, et autres
Publié: (2023)

Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms
par: Lee, Junyong, et autres
Publié: (2025)

XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage
par: Lee, Jae-Jun, et autres
Publié: (2024)

LiMAML: Personalization of Deep Recommender Models via Meta Learning
par: Wang, Ruofan, et autres
Publié: (2024)

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection
par: Huang, Tairan, et autres
Publié: (2026)

LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection
par: Ocansey, Isaiah Thompson, et autres
Publié: (2025)

Accelerating Convergence in Bayesian Few-Shot Classification
par: Ke, Tianjun, et autres
Publié: (2024)

MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning
par: Sinha, Sanchit, et autres
Publié: (2024)

HateTinyLLM : Hate Speech Detection Using Tiny Large Language Models
par: Sen, Tanmay, et autres
Publié: (2024)

BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy
par: Kar, Abhilash, et autres
Publié: (2026)

Trust Region Constrained Bayesian Optimization with Penalized Constraint Handling
par: Chowdhury, Raju, et autres
Publié: (2026)

MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning
par: Zhang, Baoquan, et autres
Publié: (2023)

MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning
par: Banerjee, Arundhati, et autres
Publié: (2023)

Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions
par: Weatherly, Chad, et autres
Publié: (2026)

Robust Anomaly Detection in Industrial Environments via Meta-Learning
par: Aqeel, Muhammad, et autres
Publié: (2025)

Supervised Anomaly Detection for Complex Industrial Images
par: Baitieva, Aimira, et autres
Publié: (2024)

AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection
par: Singh, Aditya, et autres
Publié: (2024)

None To Optima in Few Shots: Bayesian Optimization with MDP Priors
par: Li, Diantong, et autres
Publié: (2025)

Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQR
par: Toso, Leonardo F., et autres
Publié: (2024)

Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images
par: Jiao, Jiyu, et autres
Publié: (2024)

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling
par: Lendering, Camile, et autres
Publié: (2026)

TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection
par: Jung, Yoon Gyo, et autres
Publié: (2025)

Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings
par: Tsoumplekas, Georgios, et autres
Publié: (2024)

SSMT: Few-Shot Traffic Forecasting with Single Source Meta-Transfer
par: Bhaumik, Kishor Kumar, et autres
Publié: (2024)

Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning
par: Tang, Jialu, et autres
Publié: (2024)

System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization
par: Qing, Jixiang, et autres
Publié: (2024)

Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection
par: Aqeel, Muhammad, et autres
Publié: (2025)