# Autoencoder Watchdog Outlier Detection for Classifiers

arXiv: https://arxiv.org/abs/2010.12754 · HTML: https://mlsanity.com/paper/2010.12754/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Symbiotic Hybrid Neural Network Watchdog For Outlier Detection](https://mlsanity.com/paper/2103.00582/) (2021)
- [Classification of Common Waveforms Including a Watchdog for Unknown Signals](https://mlsanity.com/paper/2108.07339/) (2021)
- [Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection](https://mlsanity.com/paper/2108.09375/) (2021)
- [Generatively Augmented Neural Network Watchdog for Image Classification Networks](https://mlsanity.com/paper/2109.06168/) (2021)
- [Open-set Classification of Common Waveforms Using A Deep Feed-forward Network and Binary Isolation Forest Models](https://mlsanity.com/paper/2110.00252/) (2021)

## Abstract (arXiv)

Neural networks have often been described as black boxes. A generic neural network trained to differentiate between kittens and puppies will classify a picture of a kumquat as a kitten or a puppy. An autoencoder watch dog screens trained classifier/regression machine input candidates before processing, e.g. to first test whether the neural network input is a puppy or a kitten. Preliminary results are presented using convolutional neural networks and convolutional autoencoder watchdogs using MNIST images.