# OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

arXiv: https://arxiv.org/abs/2104.00528 · HTML: https://mlsanity.com/paper/2104.00528/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution](https://mlsanity.com/paper/1804.09081/) (2018)
- [MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection](https://mlsanity.com/paper/1909.09347/) (2019)
- [ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection](https://mlsanity.com/paper/1908.03299/) (2019)
- [FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis](https://mlsanity.com/paper/1809.05989/) (2018)
- [Task Transfer by Preference-Based Cost Learning](https://mlsanity.com/paper/1805.04686/) (2018)
- [Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning](https://mlsanity.com/paper/2006.03429/) (2020)
- [Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds](https://mlsanity.com/paper/2006.10417/) (2020)

## Built on by (most cited first, 8 of 8)

- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [A Machine Learning-oriented Survey on Tiny Machine Learning](https://mlsanity.com/paper/2309.11932/) (2023)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [TinyDefectNet: Highly Compact Deep Neural Network Architecture for High-Throughput Manufacturing Visual Quality Inspection](https://mlsanity.com/paper/2111.14319/) (2021)
- [Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping](https://mlsanity.com/paper/2307.15807/) (2023)
- [GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU](https://mlsanity.com/paper/2605.14839/) (2026)
- [TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine](https://mlsanity.com/paper/2508.16553/) (2025)
- [Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring](https://mlsanity.com/paper/2608.18341/) (2026)

## Abstract (arXiv)

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.