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.
Original languageEnglish
Pages1-14
Number of pages14
Publication statusPublished - 29 Sep 2021
EventThird 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, Spain
Duration: 17 Sep 202117 Sep 2021
https://lidta.dcc.fc.up.pt/

Workshop

WorkshopThird 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.
Abbreviated titleLIDTA 2021
Country/TerritorySpain
CityBilbao
Period17/09/2117/09/21
Internet address

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