@inproceedings{fe081970da3a471d8bf72be2b245f33d,
title = "Visual analysis of deep neural networks for device-free wireless localization",
abstract = "Device-free indoor localization is a key enabling technology for many Internet of Things (IoT) applications. Deep neural network (DNN)-based location estimators achieve high-precision localization performance by automatically learning discriminative features from noisy wireless signals without much human intervention. However, the inner workings of DNN are not transparent and not adequately understood especially in wireless localization applications. In this paper, we conduct visual analyses of DNN-based location estimators trained with Wi-Fi channel state information (CSI) fingerprints in a real-world experiment. We address such questions as 1) how well has the DNN learned and been trained, and 2) what critical features has the DNN learned to distinguish different classes, via visualization techniques. The results provide plausible explanations and allow for a better understanding of the mechanism of DNN-based wireless indoor localization.",
author = "Liu, {Shing Jiuan} and Chang, {Ronald Y.} and Feng-Tsun Chien",
year = "2019",
month = dec,
doi = "10.1109/GLOBECOM38437.2019.9013448",
language = "English",
series = "2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings",
address = "United States",
note = "null ; Conference date: 09-12-2019 Through 13-12-2019",
}