Autoencoder Watchdog Outlier Detection for Classifiers

Justin Bui, Robert Marks II

Abstract

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 watchdog 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.

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Paper Citation


in Harvard Style

Bui J. and Marks II R. (2021). Autoencoder Watchdog Outlier Detection for Classifiers.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 990-996. DOI: 10.5220/0010300509900996


in Bibtex Style

@conference{icaart21,
author={Justin Bui and Robert Marks II},
title={Autoencoder Watchdog Outlier Detection for Classifiers},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={990-996},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010300509900996},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Autoencoder Watchdog Outlier Detection for Classifiers
SN - 978-989-758-484-8
AU - Bui J.
AU - Marks II R.
PY - 2021
SP - 990
EP - 996
DO - 10.5220/0010300509900996