A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing

A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing

In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. Ma...

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Bibliographic Details
Main Authors: Tae-yong Kim, Jieun Lee, Seokhyun Gong, Jaehoon Lim, Dowan Kim, Jongpil Jeong
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Machines
Subjects:
machine vision
deep learning
few-shot learning
GAN
anomaly detection
Online Access:https://www.mdpi.com/2075-1702/13/1/21
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