Title: Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things,
Abstract: In this paper, we propose Squeezed Convolutional Variational AutoEncoder
(SCVAE) for anomaly detection in time series data for Edge Computing in
Industrial Internet of Things (IIoT). The proposed model is applied to labeled
time series data from UCI datasets for exact performance evaluation, and
applied to real world data for indirect model performance comparison. In
addition, by comparing the models before and after applying Fire Modules from
SqueezeNet, we show that model size and inference times are reduced while
similar levels of performance is maintained.