Attention-based vector quantized variational autoencoder for anomaly detection by using orthogonal subspace constraints

This paper introduces a new framework that uses a vector quantized variational autoencoder (VQVAE) enhanced by orthogonal subspace constraints (OSC) and pyramid criss-cross attention (PCCA). The framework was designed for anomaly detection in industrial product image datasets. Previous studies on mo...

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Veröffentlicht in:	Pattern recognition Jg. 164; S. 111500
Hauptverfasser:	Yu, Qien, Dai, Shengxin, Dong, Ran, Ikuno, Soichiro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.08.2025
Schlagworte:	Anomaly detection Attention mechanism Industrial image Subspace projection Vector quantized variational autoencoder Attention mechanism Vector quantized variational autoencoder Industrial image Subspace projection Anomaly detection
ISSN:	0031-3203
Online-Zugang:	Volltext
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