2021/03/31 by Saad Abbasi, Abbasi, Saad, Mahmoud Famouri +5 · 1 citation
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Sound (cs.SD) #Water Systems and Optimization
paper · pdf · doi:10.48550/arxiv.2104.00528
openalex publication_date 2021/03/31 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Human operators often diagnose industrial machinery via anomalous sounds.\nAutomated acoustic anomaly detection can lead to reliable maintenance of\nmachinery. However, deep learning-driven anomaly detection methods often\nrequire an extensive amount of computational resources which prohibits their\ndeployment in factories. Here we explore a machine-driven design exploration\nstrategy to create OutlierNets, a family of highly compact deep convolutional\nautoencoder network architectures featuring as few as 686 parameters, model\nsizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection\naccuracy matching or exceeding published architectures with as many as 4\nmillion parameters. Furthermore, CPU-accelerated latency experiments show that\nthe OutlierNet architectures can achieve as much as 21x lower latency than\npublished networks.\n