Unsupervised Visual Anomaly Detection Using Self-Supervised Pre-Trained Transformer

Unsupervised Visual Anomaly Detection Using Self-Supervised Pre-Trained Transformer

In the various industrial manufacturing processes, the automatic visual inspection system is an essential part as it reduces the chances of delivering defective products and the cost of training and hiring experts for manual inspection. In this work, we propose a new unsupervised anomaly detection m...

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Bibliographic Details
Main Authors: Jun-Hyung Kim, Goo-Rak Kwon
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Anomaly detection
data-augmentation
self-supervised learning
transformer
Online Access:https://ieeexplore.ieee.org/document/10666671/

Internet

https://ieeexplore.ieee.org/document/10666671/

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