GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case

Arnaud BOUGAHAM; Adrien Bibal; Isabelle LINDEN; Benoît Frénay

GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case

Arnaud BOUGAHAM, Adrien Bibal, Isabelle LINDEN, Benoît Frénay

Résultats de recherche: Contribution à un événement scientifique (non publié) › Article › Revue par des pairs

Résumé

Industry 4.0 and recent deep learning progress make it possible to solve problems that traditional methods could not. This is the case for anomaly detection that received a particular attention from the machine learning community, and resulted in a use of generative adversarial networks (GANs). In this work, we propose to use intermediate patches for the inference step, after a WGAN training procedure suitable for highly imbalanced datasets, to make the anomaly detection possible on full size Printed Circuit Board Assembly (PCBA) images. We therefore show that our technique can be used to support or replace actual industrial image processing algorithms, as well as to avoid a waste of time for industries.

langue originale	Anglais
Pages	1-14
Nombre de pages	14
Etat de la publication	Publié - 29 sept. 2021
Evénement	Third International Workshop on Learning with Imbalanced Domains: Theory and Applications; co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2021. - Virtual (formerly Bilbao-Spain), Bilbao, Espagne Durée: 17 sept. 2021 → 17 sept. 2021 https://lidta.dcc.fc.up.pt/

Atelier de travail

Atelier de travail	Third International Workshop on Learning with Imbalanced Domains: Theory and Applications; co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2021.
Titre abrégé	LIDTA 2021
Pays/Territoire	Espagne
La ville	Bilbao
période	17/09/21 → 17/09/21
Adresse Internet	https://lidta.dcc.fc.up.pt/

SDG des Nations Unies

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https://proceedings.mlr.press/v154/bougaham21a/bougaham21a.pdf

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BOUGAHAM, A., Bibal, A., LINDEN, I., & Frénay, B. (2021). GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case. 1-14. Papier présenté � Third International Workshop on Learning with Imbalanced Domains: Theory and Applications; co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2021., Bilbao, Espagne. https://proceedings.mlr.press/v154/bougaham21a/bougaham21a.pdf

BOUGAHAM, Arnaud ; Bibal, Adrien ; LINDEN, Isabelle ; Frénay, Benoît. / GanoDIP - GAN Anomaly Detection through Intermediate Patches : a PCBA Manufacturing Case. Papier présenté � Third International Workshop on Learning with Imbalanced Domains: Theory and Applications; co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2021., Bilbao, Espagne.14 p.

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abstract = "Industry 4.0 and recent deep learning progress make it possible to solve problems that traditional methods could not. This is the case for anomaly detection that received a particular attention from the machine learning community, and resulted in a use of generative adversarial networks (GANs). In this work, we propose to use intermediate patches for the inference step, after a WGAN training procedure suitable for highly imbalanced datasets, to make the anomaly detection possible on full size Printed Circuit Board Assembly (PCBA) images. We therefore show that our technique can be used to support or replace actual industrial image processing algorithms, as well as to avoid a waste of time for industries. ",

author = "Arnaud BOUGAHAM and Adrien Bibal and Isabelle LINDEN and Beno{\^i}t Fr{\'e}nay",

year = "2021",

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note = "Third International Workshop on Learning with Imbalanced Domains: Theory and Applications; co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2021., LIDTA 2021 ; Conference date: 17-09-2021 Through 17-09-2021",

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BOUGAHAM, A , Bibal, A , LINDEN, I & Frénay, B 2021, 'GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case', Papier présenté � Third International Workshop on Learning with Imbalanced Domains: Theory and Applications; co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2021., Bilbao, Espagne, 17/09/21 - 17/09/21 p. 1-14. <https://proceedings.mlr.press/v154/bougaham21a/bougaham21a.pdf>

GanoDIP - GAN Anomaly Detection through Intermediate Patches : a PCBA Manufacturing Case. / BOUGAHAM, Arnaud ; Bibal, Adrien ; LINDEN, Isabelle ; Frénay, Benoît.

2021. 1-14 Papier présenté � Third International Workshop on Learning with Imbalanced Domains: Theory and Applications; co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2021., Bilbao, Espagne.

Résultats de recherche: Contribution à un événement scientifique (non publié) › Article › Revue par des pairs

TY - CONF

T1 - GanoDIP - GAN Anomaly Detection through Intermediate Patches

T2 - Third International Workshop on Learning with Imbalanced Domains: Theory and Applications; co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2021.

AU - BOUGAHAM, Arnaud

AU - Bibal, Adrien

AU - LINDEN, Isabelle

AU - Frénay, Benoît

PY - 2021/9/29

Y1 - 2021/9/29

N2 - Industry 4.0 and recent deep learning progress make it possible to solve problems that traditional methods could not. This is the case for anomaly detection that received a particular attention from the machine learning community, and resulted in a use of generative adversarial networks (GANs). In this work, we propose to use intermediate patches for the inference step, after a WGAN training procedure suitable for highly imbalanced datasets, to make the anomaly detection possible on full size Printed Circuit Board Assembly (PCBA) images. We therefore show that our technique can be used to support or replace actual industrial image processing algorithms, as well as to avoid a waste of time for industries.

AB - Industry 4.0 and recent deep learning progress make it possible to solve problems that traditional methods could not. This is the case for anomaly detection that received a particular attention from the machine learning community, and resulted in a use of generative adversarial networks (GANs). In this work, we propose to use intermediate patches for the inference step, after a WGAN training procedure suitable for highly imbalanced datasets, to make the anomaly detection possible on full size Printed Circuit Board Assembly (PCBA) images. We therefore show that our technique can be used to support or replace actual industrial image processing algorithms, as well as to avoid a waste of time for industries.

M3 - Paper

SP - 1

EP - 14

Y2 - 17 September 2021 through 17 September 2021

ER -

BOUGAHAM A , Bibal A , LINDEN I , Frénay B. GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case. 2021. Papier présenté � Third International Workshop on Learning with Imbalanced Domains: Theory and Applications; co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2021., Bilbao, Espagne.

GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case

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