NDLI: Adversarial semi-supervised learning method for printed circuit board unknown defect detection

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Adversarial semi-supervised learning method for printed circuit board unknown defect detection

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Adversarial semi-supervised learning method for printed circuit board unknown defect detection

Content Provider	IET Digital Library
Author	Shi, Wei Zhang, Linlin Li, Yihui Liu, Hong
Abstract	Due to the lack of training data and fuzziness of unknown defects, unknown defect detection, which aims to identify no clearly defined defects, is still a challenging task. In practical industrial scenarios, defects on a printed circuit board account generally for a small proportion, so the data sets are highly biased towards no defect class. To this end, unknown defect detection can be treated as an anomaly detection problem. According to this, a semi-supervised learning method is proposed in this study to solve the above-mentioned problems. Inspired by the conditional generative adversarial network, the authors propose an improved end-to-end architecture for detecting unknown defects. The designed architecture is composed of three networks: a generator, a discriminator, and an encoder. Among them, the generator and the discriminator are trained by competing with each other, while collaborating to learn the distribution of underlying concepts in the target class. During training, the authors only train normal samples, and unknown defects do not appear in the process. In the testing phase, unknown defects are detected by calculating the distance between generated samples and real samples under the feature space. Experimental results over several benchmark data sets show the effectiveness of the model and superiority on state-of-the-art approaches.
Starting Page	505
Ending Page	510
Page Count	6
Volume Number	2020
e-ISSN	20513305
Issue Number	Issue 13, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2020/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1181
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-01-13
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Adversarial Semisupervised Learning Method Anomaly Detection Problem Automatic Optical Inspection Computer Vision And Image Processing Technique Conditional Generative Adversarial Network Defect Class Discriminator Electronic Engineering Computing Feature Space Generator Improved End-to-end Architecture Industrial Application of IT Inspection And Quality Control Knowledge Engineering Technique Learning in AI Neural Computing Technique Neural Net Architecture Printed Circuit Board Printed Circuit Manufacture Production Engineering Computing Unknown Defect Detection
Content Type	Text
Resource Type	Article

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