Autoencoder Watchdog Outlier Detection for Classifiers

Justin Bui; Robert J. Marks II

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Paper

Autoencoder Watchdog Outlier Detection for Classifiers

Topics: Machine Learning

In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, 990-996, 2021

Authors: Justin Bui and Robert J. Marks II

Affiliation: Department of Electrical and Computer Engineering, Baylor University, Waco, Texas, U.S.A.

Keyword(s): Neural Networks, Watchdog, CNN, MNIST, Classifier, Generator, Autoencoder.

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 several formats:

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; ISSN 2184-433X, SciTePress, pages 990-996. DOI: 10.5220/0010300509900996

@conference{icaart21,
author={Justin Bui. and Robert J. {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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Bui, J.
AU - Marks II, R.
PY - 2021
SP - 990
EP - 996
DO - 10.5220/0010300509900996
PB - SciTePress