VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge
<|reference_start|>VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge: Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demanding, requiring cloud architectures prone to latency and bandwidth issues. This work presents VARADE, a novel solution implementing a light autoregressive framework based on variational inference, which is best suited for real-time execution on the edge. The proposed approach was validated on a robotic arm, part of a pilot production line, and compared with several state-of-the-art algorithms, obtaining the best trade-off between anomaly detection accuracy, power consumption and inference frequency on two different edge platforms.<|reference_end|>
@article{mascolini2024varade:,
    title={VARADE: a Variational-based AutoRegressive model for Anomaly Detection
  on the Edge},
    author={Alessio Mascolini, Sebastiano Gaiardelli, Francesco Ponzio, Nicola
  Dall'Ora, Enrico Macii, Sara Vinco, Santa Di Cataldo, Franco Fummi},
    journal={arXiv preprint arXiv:2409.14816},
    year={2024},
    doi={10.1145/3649329.3655691},
    archivePrefix={arXiv},
    eprint={2409.14816},
    primaryClass={cs.LG cs.AI}
}