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 language | English |
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Pages | 1-14 |
Number of pages | 14 |
Publication status | Published - 29 Sept 2021 |
Event | 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, Spain Duration: 17 Sept 2021 → 17 Sept 2021 https://lidta.dcc.fc.up.pt/ |
Workshop
Workshop | 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. |
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Abbreviated title | LIDTA 2021 |
Country/Territory | Spain |
City | Bilbao |
Period | 17/09/21 → 17/09/21 |
Internet address |