@inproceedings{33b271d8f2f64c698bf680f1c62417ab,
title = "A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System",
abstract = "Since 2011, due to the growth in the amount of information, the innovation of learning algorithms and the improvement of computer technology make the application of artificial intelligence feasible in a wide range of fields. This paper presents a sketch classifier technique with deep learning models. We use the depth-wise convolution layer to lighten the deep neural network. The result shows the improvement in approximately 1/5 of computation. We use Google Quick Draw dataset to train and evaluate the network, which can have 98% accuracy in 10 categories and 85% accuracy in 100 categories. Finally, we realize it on STM32F469I Discovery development board for demonstration. The system can achieve real-time implementation of sketch classification.",
keywords = "Deep Learning, Embedded System, Neural Network, Sketch Classification",
author = "Tsai, {Tsung Han} and Chi, {Po Ting} and Cheng, {Kuo Hsing}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019 ; Conference date: 24-04-2019 Through 26-04-2019",
year = "2019",
month = apr,
doi = "10.1109/DDECS.2019.8724656",
language = "???core.languages.en_GB???",
series = "Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2019 22nd International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2019",
}