Reconstruction from a distilled encoder with edge-based pseudo-anomaly for industrial anomaly detection.
- DOI
- 10.1016/j.neunet.2026.109390
- Published
- 2027 Jan
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109390,
title = {Reconstruction from a distilled encoder with edge-based pseudo-anomaly for industrial anomaly detection.},
author = {Jiang J and Sun J and Cui Y and Zhao Y and Liu X},
year = {2027},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109390},
url = {https://doi.org/10.1016/j.neunet.2026.109390}
}RIS
TY - JOUR TI - Reconstruction from a distilled encoder with edge-based pseudo-anomaly for industrial anomaly detection. AU - Jiang J AU - Sun J AU - Cui Y AU - Zhao Y AU - Liu X PY - 2027 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109390 UR - https://doi.org/10.1016/j.neunet.2026.109390 ER -
APA
J, J., J, S., Y, C., Y, Z., & X, L. (2027). Reconstruction from a distilled encoder with edge-based pseudo-anomaly for industrial anomaly detection.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109390
Source records
- pubmed · retrieved 2026-09-25T22:56:27.649Z