# Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things

arXiv: https://arxiv.org/abs/1712.06343 · HTML: https://mlsanity.com/paper/1712.06343/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models](https://mlsanity.com/paper/2112.09293/) (2021)
- [VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge](https://mlsanity.com/paper/2409.14816/) (2024)

## Abstract (arXiv)

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.