  Human operators often diagnose industrial machinery via anomalous sounds.
Automated acoustic anomaly detection can lead to reliable maintenance of
machinery. However, deep learning-driven anomaly detection methods often
require an extensive amount of computational resources which prohibits their
deployment in factories. Here we explore a machine-driven design exploration
strategy to create OutlierNets, a family of highly compact deep convolutional
autoencoder network architectures featuring as few as 686 parameters, model
sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection
accuracy matching or exceeding published architectures with as many as 4
million parameters. Furthermore, CPU-accelerated latency experiments show that
the OutlierNet architectures can achieve as much as 21x lower latency than
published networks.
