OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionCPUMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Efficient training for compact compression models via sequential distillation
Deep learning models for image compression often face practical limitations in hardware-constrained applications. Although these models achieve high-quality reconstructions, they are typically complex, heavyweight, and r…
Knowledge DistillationImage CompressionUnsupervised Network Anomaly Detection with Autoencoders and Traffic Images
Due to the recent increase in the number of connected devices, the need to promptly detect security issues is emerging. Moreover, the high number of communication flows creates the necessity of processing huge amounts of…
Anomaly DetectionRobust Anomaly Detection through Multi-Modal Autoencoder Fusion for Small Vehicle Damage Detection
Wear and tear detection in fleet and shared vehicle systems is a critical challenge, particularly in rental and car-sharing services, where minor damage, such as dents, scratches, and underbody impacts, often goes unnoti…
Autonomous VehiclesAnomaly DetectionNeural Network-Powered Finger-Drawn Biometric Authentication
This paper investigates neural network-based biometric authentication using finger-drawn digits on touchscreen devices. We evaluated CNN and autoencoder architectures for user authentication through simple digit patterns…
Anomaly DetectionDevice Image-IV Mapping using Variational Autoencoder for Inverse Design and Forward Prediction
This paper demonstrates the learning of the underlying device physics by mapping device structure images to their corresponding Current-Voltage (IV) characteristics using a novel framework based on variational autoencode…