@inproceedings{563c9adbbc9546adba704465662ff2c2,
title = "Product Quality Prediction with Deep Transfer Learning for Smart Factories",
abstract = "This paper proposes to use deep transfer learning (DTL) 'layer freezing' method to build deep neural network (DNN) models for a target domain with few data on the basis of well-trained DNN models for a source domain with abundant data. Experiments using the DTL method are conducted for building DNN models to predict product surface roughness of wire electrical discharge machining (WEDM). The experimental results show that DTL indeed can help fast build models with high prediction accuracy for the target domain having few data.",
author = "Jiang, {Jehn Ruey} and Cheng, {Zi Kuan}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 ; Conference date: 28-09-2020 Through 30-09-2020",
year = "2020",
month = sep,
day = "28",
doi = "10.1109/ICCE-Taiwan49838.2020.9258200",
language = "???core.languages.en_GB???",
series = "2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020",
}